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Advances in Artificial Intelligence: A Review for the Creative Industries

Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing reviews have not comprehensively addressed these recent breakthroughs and their integrated impact across the creative production pipeline. This paper addresses this gap by providing a s…

Licence: OPEN CC-BY-4.0
Authors: Nantheera Anantrasirichai, Fan Zhang, David Bull
Published: 2025-01-06 · arXiv
Language: en
Length: 42227 words
Type: narrative text

Cites 155 works

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F. Yu, and L. Van Gool Transforming model prediction for tracking. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 8721–8730. External Links: Document Cited by: Table 1, §3.4.3.

T. Meinhardt, A. Kirillov, L. Leal-Taixé, and C. Feichtenhofer TrackFormer: multi-object tracking with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8844–8854. Cited by: Table 1, §3.4.3.
L. Melas-Kyriazi, I. Laina, C. Rupprecht, and A. Vedaldi RealFusion 360${}^{\circ}$ reconstruction of any object from a single image. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 8446–8455. External Links: Document Cited by: Table 1, §3.1.5.
F. Mentzer, G. D. Toderici, D. Minnen, S. Caelles, S. J. Hwang, M. Lucic, and E. Agustsson VCT: a video compression transformer. Advances in Neural Information Processing Systems 35, pp. 13091–13103. Cited by: Table 1, §3.6.2.
F. Mentzer, G. D. Toderici, M. Tschannen, and E. Agustsson High-fidelity generative image compression. Advances in Neural Information Processing Systems 33, pp. 11913–11924. Cited by: §3.6.1.
D. Metzler, Y. Tay, D. Bahri, and M. Najork Rethinking search: making domain experts out of dilettantes. SIGIR Forum 55 (1). External Links: Document Cited by: Table 1, §3.2.3.
B. Mildenhall, P. Hedman, R. Martin-Brualla, P. P. Srinivasan, and J. T. Barron NeRF in the Dark: High dynamic range view synthesis from noisy raw images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16190–16199. Cited by: Table 1, §3.5.2.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision – ECCV 2020, A. Vedaldi, H. Bischof, T. Brox, and J. Frahm (Eds.), pp. 405–421. Cited by: §2.4, Table 1, Figure 7, §3.5.2.
P. Mirowski, K. W. Mathewson, J. Pittman, and R. Evans Co-writing screenplays and theatre scripts with language models: evaluation by industry professionals. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, External Links: ISBN 9781450394215, Document Cited by: §3.1.1.
T. Miyata ZEN-iqa: zero-shot explainable and no-reference image quality assessment with vision language model. IEEE Access 12, pp. 70973–70983. Cited by: §3.7.1.
E. Molad, E. Horwitz, D. Valevski, A. R. Acha, Y. Matias, Y. Pritch, Y. Leviathan, and Y. Hoshen Dreamix: video diffusion models are general video editors. arXiv: 2302.01329. Cited by: Table 1, §3.1.4.
E. Moliner, J. Lehtinen, and V. Välimäki Solving audio inverse problems with a diffusion model. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. , pp. 1–5. External Links: Document Cited by: Table 1, Table 1, §3.3.4.
J. Moon, T. Moon, and W. Seo Generalizable style transfer for implicit neural representation. In International Conference on Learning Representations (ICLR), Cited by: Table 1, Table 1, §3.3.2.
C. Morris, N. Anantrasirichai, F. Zhang, and D. Bull DaBiT: Depth and blur informed transformer for video deblurring. In IEEE/CVF Winter Conference on Applications of Computer Vision Workshop, Cited by: Table 1, §3.3.4.
B. B. Moser, A. S. Shanbhag, F. Raue, S. Frolov, S. Palacio, and A. Dengel Diffusion models, image super-resolution, and everything: a survey. IEEE Transactions on Neural Networks and Learning Systems (), pp. 1–21. External Links: Document Cited by: §3.3.3.
T. Müller, A. Evans, C. Schied, and A. Keller Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41 (4), pp. 102:1–102:15. External Links: Document Cited by: Table 1, §3.1.5, §3.5.2.
N. G. Nair, K. Mei, and V. M. Patel AT-DDPM: Restoring faces degraded by atmospheric turbulence using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3434–3443. Cited by: Table 1, §3.3.4.
J. Nawała, Y. Jiang, F. Zhang, X. Zhu, J. Sole, and D. Bull BVI-AOM: a new training dataset for deep video compression optimization. In IEEE Visual Communications and Image Processing (VCIP)), Cited by: §3.6.2.
D. Neimark, O. Bar, M. Zohar, and D. Asselmann Video transformer network. In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Vol. , pp. 3156–3165. External Links: Document Cited by: Table 1.
S. Oh, H. Yang, and E. Park Parameter-efficient instance-adaptive neural video compression. In Asian Conference on Computer Vision, External Links: Document Cited by: §3.6.2.
OpenAI, J. Achiam, S. Adler, S. Agarwal, et al. GPT-4 technical report. arXiv: 2303.08774. Cited by: §1, Table 1, §3.7.1.
M. Oquab, T. Darcet, T. Moutakanni, H. V. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. HAZIZA, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P. Huang, S. Li, I. Misra, M. Rabbat, V. Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research. Note: External Links: ISSN 2835-8856 Cited by: Table 1, Table 1, Table 1, §3.4, §3.5.1.
Y. Ou, Z. Ma, T. Liu, and Y. Wang Perceptual quality assessment of video considering both frame rate and quantization artifacts. IEEE Transactions on Circuits and Systems for Video Technology 21 (3), pp. 286–298. Cited by: §3.7.1.
J. Pan, B. Xu, J. Dong, J. Ge, and J. Tang Deep discriminative spatial and temporal network for efficient video deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22191–22200. Cited by: §3.3.4.
W. Pan, Z. Yang, D. Liu, C. Fang, Y. Zhang, and P. Dai Quality-aware clip for blind image quality assessment. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 396–408. Cited by: §3.7.1.
T. Peng, C. Feng, D. Danier, F. Zhang, B. Vallade, A. Mackin, and D. Bull RMT-BVQA: recurrent memory transformer-based blind video quality assessment for enhanced video content. In European Conference on Computer Vision (ECCV) Workshop on Advances in Image Manipulation, Cited by: Table 1, §3.7.1.
T. Peng, G. Gao, H. Sun, F. Zhang, and D. Bull Accelerating learnt video codecs with gradient decay and layer-wise distillation. In 2024 Picture Coding Symposium (PCS), pp. 1–5. Cited by: §3.6.2.
N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, and C.-C. J. Kuo Color image database tid2013: peculiarities and preliminary results. In European Workshop on Visual Information Processing (EUVIP), Vol. , pp. 106–111. External Links: Document Cited by: §3.7.1.
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer D-NeRF: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Cited by: Table 1, §3.5.2.
L. Qi, Z. Jia, J. Li, B. Li, H. Li, and Y. Lu Long-term temporal context gathering for neural video compression. In European Conference on Computer Vision (ECCV), Cited by: §3.6.2.
G. Qian, J. Mai, A. Hamdi, J. Ren, A. Siarohin, B. Li, H. Lee, I. Skorokhodov, P. Wonka, S. Tulyakov, and B. Ghanem Magic123: One image to high-quality 3D object generation using both 2d and 3D diffusion priors. In The Twelfth International Conference on Learning Representations (ICLR), Cited by: Table 1, §3.1.5.
W. Quan, J. Chen, Y. Liu, and et al. Deep learning-based image and video inpainting: a survey. International Journal of Computer Vision. Cited by: §3.3.5.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, Cited by: §3.1.1, §3.7.1.
S. Rajput, N. Mehta, A. Singh, R. Hulikal Keshavan, T. Vu, et al. Recommender systems with generative retrieval. In Advances in Neural Information Processing Systems, Vol. 36, pp. 10299–10315. Cited by: Table 1, §3.2.3.
D. Rao, T. Xu, and X. Wu TGFuse: An infrared and visible image fusion approach based on transformer and generative adversarial network. IEEE Transactions on Image Processing (), pp. 1–1. External Links: Document Cited by: Table 1, §3.3.6.
N. Ravi, V. Gabeur, Y. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. Rädle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V. Alwala, N. Carion, C. Wu, R. Girshick, P. Dollár, and C. Feichtenhofer SAM 2: segment anything in images and videos. arXiv preprint arXiv:2408.00714. Cited by: Table 1, §3.4.1.
S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-maron, M. Giménez, Y. Sulsky, J. Kay, J. T. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar, and N. de Freitas A generalist agent. Transactions on Machine Learning Research. External Links: ISSN 2835-8856 Cited by: §2.1.
A. Rehman, K. Zeng, and Z. Wang Display device-adapted video quality-of-experience assessment. In Human vision and electronic imaging XX, Vol. 9394, pp. 27–37. Cited by: §3.7.1.
J. Ren, L. Pan, J. Tang, C. Zhang, A. Cao, G. Zeng, and Z. Liu DreamGaussian4D: Generative 4d gaussian splatting. arXiv preprint arXiv:2312.17142. Cited by: Table 1, §3.1.5.
J. Ren, Q. Zheng, Y. Zhao, X. Xu, and C. Li DLFormer: Discrete latent transformer for video inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3511–3520. Cited by: Table 1, §3.3.5.
S. Ren, K. He, R. Girshick, and J. Sun Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6), pp. 1137–1149. External Links: Document Cited by: §3.4.2.
Y. Ren, X. Xia, Y. Lu, J. Zhang, J. Wu, P. Xie, X. Wang, and X. Xiao Hyper-SD: Trajectory segmented consistency model for efficient image synthesis. In Advances in Neural Information Processing Systems, Cited by: Table 1, §3.1.3.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 10674–10685. External Links: Document Cited by: Figure 1, §2.3, §2.3, Table 1.
H. Ruan, Y. Shao, Q. Yang, L. Zhao, and D. Niyato Point cloud compression with implicit neural representations: a unified framework. arXiv preprint arXiv:2405.11493. Cited by: Table 1, §3.6.2.
J. Ryu, K. Kim, D. Heo, H. Song, C. Oh, and B. Suh Cinema multiverse lounge: enhancing film appreciation via multi-agent conversations. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25. External Links: Document Cited by: §3.2.2.
N. Safonov, A. Bryncev, A. Moskalenko, D. Kulikov, et al. NTIRE 2025 challenge on UGC video enhancement: methods and results. arXiv preprint arXiv:2505.03007. Cited by: §3.3.1.
C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, and M. Norouzi Image super-resolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4), pp. 4713–4726. External Links: Document Cited by: Table 1, Table 1, §3.3.3.
R. Sajja, Y. Sermet, M. Cikmaz, D. Cwiertny, and I. Demir Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information 15 (10), pp. 596. External Links: Document Cited by: §3.2.4.
Sara Fridovich-Keil and Giacomo Meanti, F. R. Warburg, B. Recht, and A. Kanazawa K-planes: explicit radiance fields in space, time, and appearance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: Table 1, §3.5.2.
V. Saragadam, D. LeJeune, J. Tan, G. Balakrishnan, A. Veeraraghavan, and R. G. Baraniuk WIRE: wavelet implicit neural representations. In Conf. Computer Vision and Pattern Recognition, Cited by: §2.4, §3.3.4.
J. L. Schönberger and J. Frahm Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: §3.5.2.
J. Selva, A. S. Johansen, S. Escalera, K. Nasrollahi, T. B. Moeslund, and A. Clapes Video transformers: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (11), pp. 12922–12943. External Links: ISSN 1939-3539, Document Cited by: §2.1.
K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack Study of subjective and objective quality assessment of video. IEEE Trans. on Image Processing 19 (6), pp. 1427–1441. Cited by: §3.7.1.
S. C. Shapiro and D. Eckroth (Eds.) Encyclopedia of artificial intelligence. Vol. 1, John Wiley & Sons, New York. Cited by: §3.1.
H. R. Sheikh, M. F. Sabir, and A. C. Bovik A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on image processing 15 (11), pp. 3440–3451. Cited by: §3.7.1.
J. Shi, P. Gao, and J. Qin Transformer-based no-reference image quality assessment via supervised contrastive learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, pp. 4829–4837. Cited by: Table 1, §3.7.1.
X. Shi, Z. Huang, F. Wang, W. Bian, D. Li, Y. Zhang, M. Zhang, K. C. Cheung, S. See, H. Qin, J. Dai, and H. Li Motion-I2V: Consistent and controllable image-to-video generation with explicit motion modeling. In ACM SIGGRAPH Conference Papers, External Links: Document Cited by: Table 1, Table 1, §3.3.7.
Y. Shi, B. Xia, X. Jin, X. Wang, T. Zhao, X. Xia, X. Xiao, and W. Yang VmambaIR: Visual state space model for image restoration. IEEE Transactions on Circuits and Systems for Video Technology (). External Links: Document Cited by: Table 1, §3.3.4.
Y. Shi, H. Ma, W. Zhong, Q. Tan, G. Mai, X. Li, T. Liu, and J. Huang ChatGraph: Interpretable text classification by converting chatgpt knowledge to graphs. In 2023 IEEE International Conference on Data Mining Workshops (ICDMW), Vol. , pp. 515–520. External Links: Document Cited by: Table 1, §3.2.1.
U. Singer, A. Polyak, T. Hayes, X. Yin, J. An, S. Zhang, Q. Hu, H. Yang, O. Ashual, O. Gafni, D. Parikh, S. Gupta, and Y. Taigman Make-A-Video: Text-to-video generation without text-video data. In The Eleventh International Conference on Learning Representations, Cited by: Table 1, §3.1.4.
Z. Sinno and A. C. Bovik Large-scale study of perceptual video quality. IEEE Trans. on Image Processing 28 (2), pp. 612–627. Cited by: §3.7.1.
V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein Implicit neural representations with periodic activation functions. Advances in neural information processing systems 33, pp. 7462–7473. Cited by: Table 1, §3.6.1.
V. Sitzmann, J. N.P. Martel, A. W. Bergman, D. B. Lindell, and G. Wetzstein Implicit neural representations with periodic activation functions. In Proc. NeurIPS, Cited by: §2.4.
H. Siuzdak, F. Grötschla, and L. A. Lanzendörfer SNAC: multi-scale neural audio codec. In Audio Imagination: NeurIPS 2024 Workshop AI-Driven Speech, Music, and Sound Generation, Note: https://openreview.net/forum?id=PFBF5ctj4X Cited by: §3.6.3.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning, Vol. 37, pp. 2256–2265. Cited by: §2.3.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations, Cited by: §2.3.
Y. Song, Z. He, H. Qian, and X. Du Vision transformers for single image dehazing. IEEE Transactions on Image Processing 32 (), pp. 1927–1941. External Links: Document Cited by: Table 1, §3.3.4.
Z. Song, L. Liu, F. Jia, Y. Luo, C. Jia, G. Zhang, L. Yang, and L. Wang Robustness-aware 3d object detection in autonomous driving: a review and outlook. IEEE Transactions on Intelligent Transportation Systems 25 (11), pp. 15407–15436. External Links: Document Cited by: §3.4.2.
M. Stefanini, M. Cornia, L. Baraldi, S. Cascianelli, G. Fiameni, and R. Cucchiara From show to tell: a survey on deep learning-based image captioning. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (1), pp. 539–559. External Links: Document Cited by: §3.1.1.
Y. Strümpler, J. Postels, R. Yang, L. V. Gool, and F. Tombari Implicit neural representations for image compression. In European Conference on Computer Vision, pp. 74–91. Cited by: Table 1, §3.6.1.
L. Sun, H. Guo, B. Ren, et al. The tenth ntire 2025 image denoising challenge report. arXiv preprint arXiv:2504.12276. Cited by: §3.3.4.
X. Sun, X. Li, J. Li, F. Wu, S. Guo, T. Zhang, and G. Wang Text classification via large language models. In The 2023 Conference on Empirical Methods in Natural Language Processing, Cited by: Table 1, §3.2.1.
H. Tan, G. Xiang, X. Xie, and H. Jia Joint frame-level and block-level rate-perception optimized preprocessing for video coding. In Proceedings of the 6th ACM International Conference on Multimedia in Asia, Cited by: §3.6.2.
J. Tang, J. Ren, H. Zhou, Z. Liu, and G. Zeng DreamGaussian: generative gaussian splatting for efficient 3D content creation. In The Twelfth International Conference on Learning Representations, Cited by: Table 1, Table 1, §3.1.5.
X. Tang, M. Yang, P. Sun, H. Li, Y. Dai, F. Zhu, and H. Lee PaReNeRF: toward fast large-scale dynamic nerf with patch-based reference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5428–5438. Cited by: Table 1, §3.5.2.
Q. Team Qwen2.5 technical report. arXiv preprint arXiv:2412.15115. Cited by: §4.3.
Y. Tian, Q. Ye, and D. Doermann YOLOv12: attention-centric real-time object detectors. arXiv preprint arXiv:2502.12524. Cited by: Table 1, §3.4.2.
R. Tous Lester: Rotoscope animation through video object segmentation and tracking. Algorithms 17 (8). External Links: Document Cited by: §3.3.7.
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al. Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971. Cited by: §3.7.1.
A. van den Oord, O. Vinyals, and K. Kavukcuoglu Neural discrete representation learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6309–6318. Cited by: §3.1.2, §3.6.3.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin Attention is all you need. In Advances in Neural Information Processing Systems, Vol. 30, pp. . Cited by: Figure 1, §2.1, Table 1, §3.4.3.
R. Villegas, M. Babaeizadeh, P. Kindermans, H. Moraldo, H. Zhang, M. T. Saffar, S. Castro, J. Kunze, and D. Erhan Phenaki: Variable length video generation from open domain textual descriptions. In International Conference on Learning Representations, Cited by: Table 1, §3.1.4.
H. L. F. von Helmholtz Handbook of physiological optics. 1st edition, Voss, Hamburg and Leipzig, Germany. Cited by: §3.7.1.
P. V. Vu, C. T. Vu, and D. M. Chandler A spatiotemporal most-apparent-distortion model for video quality assessment. In IEEE ICIP, pp. 2505–2508. Cited by: §3.7.1.
A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, and G. Ding YOLOv10: Real-time end-to-end object detection. In Advances in Neural Information Processing Systems, Cited by: §3.4.2.
D. Wang, X. Cui, X. Chen, Z. Zou, T. Shi, S. Salcudean, Z. J. Wang, and R. Ward Multi-view 3D reconstruction with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5722–5731. Cited by: Table 1.
H. Wang, N. Anantrasirichai, F. Zhang, and D. Bull UW-GS: Distractor-aware 3d gaussian splatting for enhanced underwater scene reconstruction. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Cited by: Table 1, §3.5.3.
J. Wang, Z. Lin, M. Wei, Y. Zhao, C. Yang, C. C. Loy, and L. Jiang SeedVR: Seeding infinity in diffusion transformer towards generic video restoration. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: Table 1, Table 1.
J. Wang, H. Yuan, D. Chen, Y. Zhang, X. Wang, and S. Zhang ModelScope text-to-video technical report. arXiv: 2308.06571. Cited by: Table 1, §3.1.4.
T. Wang, L. Li, K. Lin, Y. Zhai, C. Lin, Z. Yang, H. Zhang, Z. Liu, and L. Wang DisCo: disentangled control for realistic human dance generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9326–9336. Cited by: Table 1, §3.1.4.
T. Wang, K. Zhang, T. Shen, W. Luo, B. Stenger, and T. Lu Ultra-high-definition low-light image enhancement: a benchmark and transformer-based method. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, pp. 2654–2662. Cited by: Table 1, §3.3.1.
X. Wang, W. Wang, Y. Cao, C. Shen, and T. Huang Images speak in images: a generalist painter for in-context visual learning. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 6830–6839. External Links: Document Cited by: §1, Table 1, Table 1.
X. Wang, X. Zhang, Y. Cao, W. Wang, C. Shen, and T. Huang SegGPT: towards segmenting everything in context. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Vol. , pp. 1130–1140. External Links: Document Cited by: Table 1, §3.4.1.
Y. Wang, X. Chen, X. Ma, S. Zhou, Z. Huang, Y. Wang, C. Yang, Y. He, J. Yu, P. Yang, Y. Guo, T. Wu, C. Si, Y. Jiang, C. Chen, C. C. Loy, B. Dai, D. Lin, Y. Qiao, and Z. Liu LaVie: High-quality video generation with cascaded latent diffusion models. International Journal of Computer Vision 133 (5), pp. 3059–3078. External Links: Document Cited by: Table 1, §3.1.4.
Y. Wang, S. Inguva, and B. Adsumilli YouTube UGC dataset for video compression research. In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1–5. Cited by: §3.7.1.
Y. Wang, T. Isobe, X. Jia, X. Tao, H. Lu, and Y. Tai Compression-aware video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2012–2021. Cited by: §3.6.2.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 (4), pp. 600–612. Cited by: §3.7.1.
Z. Wang, E. P. Simoncelli, and A. C. Bovik Multi-scale structural similarity for image quality assessment. In Asilomar Conf. Signals Syst. Comput., pp. 1398–1402. Cited by: §3.6.1.
Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, and H. Li Uformer: a general u-shaped transformer for image restoration. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 17662–17672. Cited by: Table 1, §3.3.4, §3.3.4.
Z. Wang, Q. Xie, T. Li, H. Du, L. Xie, P. Zhu, and M. Bi One-shot voice conversion for style transfer based on speaker adaptation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6792–6796. External Links: Document Cited by: §3.1.2.
Z. Wang, E. P. Simoncelli, and A. C. Bovik Multiscale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Vol. 2, pp. 1398–1402. Cited by: §3.7.1.
Z. Wang, J. Yu, A. W. Yu, Z. Dai, Y. Tsvetkov, and Y. Cao SIMVLM: Simple visual language model pretraining with weak supervision. In International Conference on Learning Representations (ICLR), Cited by: Table 1, §3.1.1.
H. Wei, L. Kong, J. Chen, L. Zhao, Z. Ge, J. Yang, J. Sun, C. Han, and X. Zhang Vary: Scaling up the vision vocabulary for large vision-language models. In European Conference on Computer Vision, Cited by: Table 1, §3.1.1.
S. Woo, J. Park, J. Lee, and I. S. Kweon CBAM: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV), Cited by: §2.1.
G. Wu, T. Yi, J. Fang, L. Xie, X. Zhang, W. Wei, W. Liu, Q. Tian, and W. Xinggang 4D gaussian splatting for real-time dynamic scene rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: Table 1, §3.5.3.
H. Wu, C. Chen, J. Hou, L. Liao, A. Wang, W. Sun, Q. Yan, and W. Lin Fast-VQA: efficient end-to-end video quality assessment with fragment sampling. In European conference on computer vision, pp. 538–554. Cited by: Table 1, §3.7.1.
H. Wu, L. Liao, J. Hou, C. Chen, E. Zhang, A. Wang, W. Sun, Q. Yan, and W. Lin Exploring opinion-unaware video quality assessment with semantic affinity criterion. In 2023 IEEE International Conference on Multimedia and Expo (ICME), pp. 366–371. Cited by: §3.7.1.
H. Wu, L. Liao, A. Wang, C. Chen, J. Hou, W. Sun, Q. Yan, and W. Lin Towards robust text-prompted semantic criterion for in-the-wild video quality assessment. arXiv preprint arXiv:2304.14672. Cited by: §3.7.1, §3.7.1.
H. Wu, E. Zhang, L. Liao, C. Chen, J. Hou, A. Wang, W. Sun, Q. Yan, and W. Lin Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 20144–20154. Cited by: Table 1, §3.7.1.
H. Wu, Z. Zhang, E. Zhang, C. Chen, L. Liao, A. Wang, C. Li, W. Sun, Q. Yan, G. Zhai, et al. Q-bench: a benchmark for general-purpose foundation models on low-level vision. In The Twelfth International Conference on Learning Representations, Cited by: §3.7.1.
H. Wu, Z. Zhang, W. Zhang, C. Chen, L. Liao, C. Li, Y. Gao, A. Wang, E. Zhang, W. Sun, et al. Q-Align: teaching LMMs for visual scoring via discrete text-defined levels. In International Conference on Machine Learning, Cited by: §3.7.1.
J. Z. Wu, Y. Ge, X. Wang, S. W. Lei, Y. Gu, Y. Shi, W. Hsu, Y. Shan, X. Qie, and M. Z. Shou Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7623–7633. Cited by: Table 1, §3.1.4.
T. Wu, Y. Zhang, X. Wang, X. Zhou, G. Zheng, Z. Qi, Y. Shan, and X. Li CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition Abilities. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, pp. 8469–8477. External Links: Document Cited by: Table 1, §3.1.4.
T. Wu, S. He, J. Liu, S. Sun, K. Liu, Q. Han, and Y. Tang A brief overview of ChatGPT: the history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica 10 (5), pp. 1122–1136. External Links: Document Cited by: §1.
W. Wu, Y. Zhao, H. Chen, Y. Gu, R. Zhao, Y. He, H. Zhou, M. Z. Shou, and C. Shen DatasetDM: Synthesizing data with perception annotations using diffusion models. In Advances in Neural Information Processing Systems, Vol. 36, pp. 54683–54695. Cited by: §3.4.2.
W. Wu, Y. Zhao, M. Z. Shou, H. Zhou, and C. Shen DiffuMask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1206–1217. Cited by: Table 1, §3.4.1.
Z. Wu, L. Zhu, Z. Yin, X. Xu, J. Zhu, X. Wei, and X. Yang MAFCD: Multi-level and adaptive conditional diffusion model for anomaly detection. Information Fusion 118, pp. 102965. Note: https://www.sciencedirect.com/science/article/pii/S1566253525000387 External Links: ISSN 1566-2535, Document Cited by: Table 1, §3.4.2.
J. Wynn and D. Turmukhambetov DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: Table 1, §3.5.2.
J. Xiang, K. Tian, and J. Zhang Mimt: masked image modeling transformer for video compression. In International Conference on Learning Representations, Cited by: Table 1, §3.6.2.
F. Xie, Z. Wang, and C. Ma DiffusionTrack: Point set diffusion model for visual object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19113–19124. Cited by: Table 1, §3.4.3.
Y. Xie, T. Takikawa, S. Saito, O. Litany, S. Yan, N. Khan, F. Tombari, J. Tompkin, V. Sitzmann, and S. Sridhar Neural fields in visual computing and beyond. In Computer Graphics Forum, Vol. 41(2), pp. 641–676. Cited by: §2.4, §3.5.2.
D. Xu, Y. Jiang, P. Wang, Z. Fan, Y. Wang, and Z. Wang NeuralLift-360: Lifting an in-the-wild 2d photo to a 3D object with 360° views. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 4479–4489. External Links: Document Cited by: Table 1, §3.1.5.
F. Xu, T. Zhou, T. Nguyen, H. Bao, C. Lin, and J. Du Integrating augmented reality and llm for enhanced cognitive support in critical audio communications. International Journal of Human-Computer Studies 194, pp. 103402. External Links: ISSN 1071-5819, Document Cited by: §3.1.5.
J. Xu, X. Hu, L. Zhu, Q. Dou, J. Dai, Y. Qiao, and P. Heng Video dehazing via a multi-range temporal alignment network with physical prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18053–18062. Cited by: Table 1, §3.3.4.
J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang, and S. De Mello Open-vocabulary panoptic segmentation with text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2955–2966. Cited by: Table 1, §3.4.1.
M. Xu, J. Chen, H. Wang, S. Liu, G. Li, and Z. Bai C3DVQA: full-reference video quality assessment with 3d convolutional neural network. In IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4447–4451. Cited by: §3.7.1.
S. Xu, G. Chen, Y. Guo, J. Yang, C. Li, Z. Zang, Y. Zhang, X. Tong, and B. Guo VASA-1: lifelike audio-driven talking faces generated in real time. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, Cited by: Table 1, Figure 4, §3.1.4.
X. Xu, R. Wang, C. Fu, and J. Jia SNR-aware low-light image enhancement. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 17693–17703. External Links: Document Cited by: Table 1, §3.3.1.
Y. Xu, T. Park, R. Zhang, Y. Zhou, E. Shechtman, F. Liu, J. Huang, and D. Liu VideoGigaGAN: Towards detail-rich video super-resolution. arXiv:2404.12388. Cited by: Table 1, §3.3.3.
B. Yan, Y. Jiang, J. Wu, D. Wang, P. Luo, Z. Yuan, and H. Lu Universal instance perception as object discovery and retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15325–15336. Cited by: Table 1, Figure 6, §3.4.
C. Yang, L. Liang, and Z. Su Real-world denoising via diffusion model. arXiv preprint arXiv:2305.04457. Cited by: Table 1, §3.3.4.
D. Yang, H. Guo, Y. Wang, R. Huang, X. Li, X. Tan, X. Wu, and H. M. Meng UniAudio 1.5: large language model-driven audio codec is a few-shot audio task learner. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, Note: https://openreview.net/forum?id=NGrINZyZKk Cited by: §3.6.3.
D. Yang, J. Tian, X. Tan, R. Huang, S. Liu, X. Chang, J. Shi, S. Zhao, J. Bian, X. Wu, et al. UniAudio: an audio foundation model toward universal audio generation. arXiv preprint arXiv:2310.00704. Cited by: §3.6.3.
D. Yang, J. Yu, H. Wang, W. Wang, C. Weng, Y. Zou, and D. Yu Diffsound: discrete diffusion model for text-to-sound generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (), pp. 1720–1733. External Links: Document Cited by: Table 1, §3.1.2.
H. Yang, L. Pan, Y. Yang, R. Hartley, and M. Liu LDP: language-driven dual-pixel image defocus deblurring network. arXiv: 2307.09815. External Links: Cited by: Table 1, §3.3.4.
J. Yang Exploring the productivity of generative ai-powered ad campaigns: a consumer response perspective. Journal of Interactive Advertising 25 (3), pp. 222–239. External Links: Document Cited by: §1.
J. Yang, M. Gao, Z. Li, S. Gao, F. Wang, and F. Zheng Track anything: segment anything meets videos. arXiv:2304.11968. Cited by: Table 1, §3.4.3.
L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao Depth anything: unleashing the power of large-scale unlabeled data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: Table 1, §3.4.2, §3.5.1.
L. Yang, B. Kang, Z. Huang, Z. Zhao, X. Xu, J. Feng, and H. Zhao Depth anything v2. In Advances in Neural Information Processing Systems, Cited by: Table 1, §3.5.1.
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M. Yang Diffusion models: a comprehensive survey of methods and applications. ACM Comput. Surv. 56 (4). External Links: Document Cited by: Figure 1.
R. Yang and S. Mandt Lossy image compression with conditional diffusion models. Advances in Neural Information Processing Systems 36. Cited by: Table 1, §3.6.1.
S. Yang, M. Ding, Y. Wu, Z. Li, and J. Zhang Implicit neural representation for cooperative low-light image enhancement. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12918–12927. Cited by: Table 1, §3.3.1.
Y. Yang, L. Pan, L. Liu, and M. Liu K3DN: Disparity-aware kernel estimation for dual-pixel defocus deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13263–13272. Cited by: §3.3.4.
Y. Yang, F. Sun, L. Weihs, E. VanderBilt, A. Herrasti, W. Han, J. Wu, N. Haber, R. Krishna, L. Liu, C. Callison-Burch, M. Yatskar, A. Kembhavi, and C. Clark Holodeck: Language guided generation of 3D embodied AI environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: Table 1, §3.1.5.
Y. Yao, J. Duan, K. Xu, Y. Cai, Z. Sun, and Y. Zhang A survey on large language model (llm) security and privacy: the good, the bad, and the ugly. High-Confidence Computing 4 (2), pp. 100211. External Links: Document Cited by: §2.2.
S. Ye, D. Kim, S. Kim, H. Hwang, S. Kim, Y. Jo, J. Thorne, J. Kim, and M. Seo FLASK: fine-grained language model evaluation based on alignment skill sets. In The Twelfth International Conference on Learning Representations, Note: https://openreview.net/forum?id=CYmF38ysDa Cited by: Figure 2, §2.2.
A. Yi and N. Anantrasirichai A comprehensive study of object tracking in low-light environments. Sensors 24 (14). Note: https://arxiv.org/pdf/2312.16250.pdf Cited by: Table 1, §3.4.3.
X. Yi, H. Xu, H. Zhang, L. Tang, and J. Ma Diff-retinex: rethinking low-light image enhancement with a generative diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12302–12311. Cited by: Table 1, §3.3.1.
Y. Yin, D. Xu, C. Tan, P. Liu, Y. Zhao, and Y. Wei CLE Diffusion: Controllable light enhancement diffusion model. In Proceedings of the 31st ACM International Conference on Multimedia, pp. 8145–8156. External Links: Document Cited by: Table 1, §3.3.1.
G. Youk, J. Oh, and M. Kim FMA-Net: Flow-guided dynamic filtering and iterative feature refinement with multi-attention for joint video super-resolution and deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: Table 1, §3.3.4.
G. Yu, A. Li, H. Wang, Y. Wang, Y. Ke, and C. Zheng DBT-net: dual-branch federative magnitude and phase estimation with attention-in-attention transformer for monaural speech enhancement. IEEE/ACM Transactions on Audio, Speech, and Language Processing 30 (), pp. 2629–2644. External Links: Document Cited by: Table 1, §3.3.4.
H. Yu, J. Julin, Z. A. Milacski, K. Niinuma, and L. A. Jeni CoGS: Controllable gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21624–21633. Cited by: Table 1, §3.5.3.
W. Yu, L. Po, R. C.C. Cheung, Y. Zhao, Y. Xue, and K. Li Bidirectionally deformable motion modulation for video-based human pose transfer. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Vol. , pp. 7468–7478. External Links: Document Cited by: Table 1, §3.1.4.
H. Yue, C. Cao, L. Liao, and J. Yang RViDeformer: Efficient raw video denoising transformer with a larger benchmark dataset. IEEE Transactions on Circuits and Systems for Video Technology (), pp. 1–1. External Links: Document Cited by: Table 1, §3.3.4.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M. Yang Restormer: efficient transformer for high-resolution image restoration. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 5718–5729. Cited by: Table 1, §3.3.4.
N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasacchi Soundstream: an end-to-end neural audio codec. IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, pp. 495–507. Cited by: §3.6.3.
F. Zeng, B. Dong, Y. Zhang, T. Wang, X. Zhang, and Y. Wei MOTR: end-to-end multiple-object tracking with transformer. In European Conference on Computer Vision (ECCV), Cited by: Table 1, §3.4.3.
G. Zhai and X. Min Perceptual image quality assessment: a survey. Science China Information Sciences 63, pp. 1–52. Cited by: §3.7.
Y. Zhan, Z. Li, M. Niu, Z. Zhong, S. Nobuhara, K. Nishino, and Y. Zheng KFD-NeRF: rethinking dynamic nerf with kalman filter. In Proceedings of the European Conference on Computer Vision (ECCV), Cited by: Table 1, §3.5.2.
F. Zhang and D. R. Bull A perception-based hybrid model for video quality assessment. IEEE Transactions on Circuits and Systems for Video Technology 26 (6), pp. 1017–1028. Cited by: §3.7.1.
H. Zhang, C. Jung, D. Zou, and M. Li WCDANN: a lightweight CNN post-processing filter for VVC-based video compression. IEEE Access. Cited by: §3.6.2.
H. Zhang, Z. Wang, D. Zeng, Z. Wu, and Y. Jiang DiffusionAD: Norm-guided one-step denoising diffusion for anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (), pp. 1–13. External Links: Document Cited by: Table 1, §3.4.2.
J. Zhang, J. Huang, S. Jin, and S. Lu Vision-language models for vision tasks: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (), pp. 1–20. External Links: Document Cited by: §3.1.1.
N. Zhang, F. Nex, G. Vosselman, and N. Kerle Lite-Mono: A lightweight cnn and transformer architecture for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18537–18546. Cited by: Table 1, §3.5.1.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586–595. Cited by: §3.7.1.
R. Zhang, D. Cai, L. Qian, Y. Du, H. Lu, and Y. Zhang DiffusionTracker: Targets denoising based on diffusion model for visual tracking. In Pattern Recognition and Computer Vision, pp. 225–237. Cited by: Table 1, §3.4.3.
T. Zhang, X. Tian, Y. Zhou, S. Ji, X. Wang, X. Tao, Y. Zhang, P. Wan, Z. Wang, and Y. Wu DVIS++: Improved decoupled framework for universal video segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. External Links: Document Cited by: Table 1, §3.4.1.
W. Zhang, K. Ma, G. Zhai, and X. Yang Uncertainty-aware blind image quality assessment in the laboratory and wild. IEEE Transactions on Image Processing 30, pp. 3474–3486. Cited by: §3.7.1.
X. Zhang and Y. Demiris Visible and infrared image fusion using deep learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (8), pp. 10535–10554. External Links: Document Cited by: §3.3.6.
X. Zhang, Z. Mao, N. Chimitt, and S. H. Chan Imaging through the atmosphere using turbulence mitigation transformer. IEEE Transactions on Computational Imaging 10 (), pp. 115–128. External Links: Document Cited by: Table 1, §3.3.4.
Y. Zhang, T. Wang, and X. Zhang MOTRv2: Bootstrapping end-to-end multi-object tracking by pretrained object detectors. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vol. , pp. 22056–22065. External Links: ISSN , Document Cited by: Table 1, §3.4.3.
Y. Zhang, N. Huang, F. Tang, H. Huang, C. Ma, W. Dong, and C. Xu Inversion-based style transfer with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10146–10156. Cited by: Table 1, §3.3.2.
Z. Zhang, Z. Hu, W. Deng, C. Fan, T. Lv, and Y. Ding DINet: Deformation inpainting network for realistic face visually dubbing on high resolution video. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, pp. 3543–3551. Cited by: §3.3.5.
Z. Zhang, Y. Zhou, C. Li, B. Zhao, X. Liu, and G. Zhai Quality assessment in the era of large models: a survey. arXiv preprint arXiv:2409.00031. Cited by: §3.7.
K. Zhao, K. Yuan, M. Sun, M. Li, and X. Wen Quality-aware pre-trained models for blind image quality assessment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 22302–22313. Cited by: §3.7.1.
L. Zhao, N. B. Gundavarapu, L. Yuan, H. Zhou, S. Yan, J. J., et al. VideoPrism: A foundational visual encoder for video understanding. In Proceedings of the 41st International Conference on Machine Learning, Cited by: Table 1, §3.4.
W. X. Zhao, K. Zhou, J. Li, T. Tang, et al. A survey of large language models. arXiv:2303.18223. Cited by: §2.2, §2.2.
Y. Zhao, W. He, C. Jia, Q. Wang, J. Li, Y. Li, C. Lin, K. Zhang, L. Zhang, and S. Ma A neural-network enhanced video coding framework beyond ECM. In 2024 Data Compression Conference (DCC), pp. 605–605. Cited by: §3.6.2.
Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, and J. Chen DETRs beat yolos on real-time object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: §3.4.2.
Y. Zhao, M. Dasari, and T. Guo CleAR: robust context-guided generative lighting estimation for mobile augmented reality. arXiv preprint arXiv:2411.02179. Cited by: Table 1, §3.1.5.
Z. Zhao, H. Bai, Y. Zhu, J. Zhang, S. Xu, Y. Zhang, K. Zhang, D. Meng, R. Timofte, and L. Van Gool DDFM: Denoising diffusion model for multi-modality image fusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8082–8093. Cited by: Table 1, §3.3.6.
C. Zheng, T. Cham, and J. Cai Pluralistic image completion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1438–1447. Cited by: §3.3.5.
Q. Zheng, Y. Fan, L. Huang, T. Zhu, J. Liu, Z. Hao, S. Xing, C. Chen, X. Min, A. C. Bovik, et al. Video quality assessment: a comprehensive survey. arXiv preprint arXiv:2412.04508. Cited by: §3.7.
L. Zhong, Z. Wang, and J. Shang LDB: A large language model debugger via verifying runtime execution step-by-step. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics: Findings., Cited by: §4.1.
F. Zhou, W. Sheng, Z. Lu, and G. Qiu A database and model for the visual quality assessment of super-resolution videos. IEEE Transactions on Broadcasting 70 (2), pp. 516–532. External Links: Document Cited by: §3.7.1.
S. Zhou, C. Li, K. C.K. Chan, and C. C. Loy ProPainter: Improving propagation and transformer for video inpainting. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10477–10486. Cited by: Table 1, §3.3.5.
S. Zhou, C. Li, and C. Change Loy LEDNet: joint low-light enhancement and deblurring in the dark. In Computer Vision – ECCV 2022, S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner (Eds.), pp. 573–589. Cited by: §3.3.1.
X. Zhou, Y. Zheng, and J. Yang Bridging the metrics gap in image style transfer: a comprehensive survey of models and criteria. Neurocomputing 624, pp. 129430. External Links: ISSN 0925-2312, Document Cited by: §3.3.2.
H. Zhu, X. Sui, B. Chen, X. Liu, Y. Fang, and S. Wang 2AFC prompting of large multimodal models for image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology. Cited by: §3.7.1.
K. Zhu, C. Li, V. Asari, and D. Saupe No-reference video quality assessment based on artifact measurement and statistical analysis. IEEE Transactions on Circuits and Systems for Video Technology 25 (4), pp. 533–546. Cited by: §3.7.1.
L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang Vision mamba: efficient visual representation learning with bidirectional state space model. Cited by: §2.1.
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai Deformable DETR: Deformable transformers for end-to-end object detection. In International Conference on Learning Representations (ICLR), Cited by: Table 1, §3.4.2.
Y. Zhu, Y. Yang, and T. Cohen Transformer-based transform coding. In International Conference on Learning Representations, Cited by: Table 1, §3.6.1.
Y. Zhu, L. Zhang, Z. Rong, T. Hu, S. Liang, and Z. Ge INFP: Audio-driven interactive head generation in dyadic conversations. arXiv:2412.04037. Cited by: Table 1, Table 1, §3.1.4.
R. Zou, C. Song, and Z. Zhang The devil is in the details: window-based attention for image compression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 17492–17501. Cited by: Table 1, §3.6.1.
X. Zou, J. Yang, H. Zhang, F. Li, L. Li, J. Wang, L. Wang, J. Gao, and Y. J. Lee Segment everything everywhere all at once. In Advances in Neural Information Processing Systems, Vol. 36, pp. 19769–19782. Cited by: Table 1, §3.4.1.
Z. Zou and N. Anantrasirichai DeTurb: Atmospheric turbulence mitigation with deformable 3d convolutions and 3d swin transformers. In Proceedings of the Asian Conference on Computer Vision (ACCV), Cited by: Table 1, §3.3.4.
Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye Object detection in 20 years: a survey. Proceedings of the IEEE 111 (3), pp. 257–276. External Links: Document Cited by: §3.4.2.

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Identifier dcterms:identifier this engine resource_id source 2 assertions: tag:aim-pro.eu,2026:oer/94988a1ebfff
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Nantheera Anantrasirichai
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Description dcterms:description

source resource/summary

Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing reviews have not comprehensively addressed these recent breakthroughs and their integrated impact across the creative production pipeline. This paper addresses this gap by providing a systematic review of AI technologies that have emerged or matured since our 2022 review, examining their applications across content creation, information analysis, post-production enhancement, compression, and quality assessment. We document how transformers, LLMs, diffusion models, and implicit neural representations have established new capabilities in text-to-image/video generation, real-time 3D reconstruction, and unified multi-task frameworks-shifting AI from support tool to core creative technology. Beyond technological advances, we analyze the trend toward unified AI frameworks that integrate multiple creative tasks, replacing task-specific solutions. We critically examine the evolving role of human-AI collaboration, where human oversight remains essential for creative direction and mitigating AI hallucinations. Finally, we identify emerging challenges including copyright concerns, bias mitigation, computational demands, and the need for robust regulatory frameworks. This review provides researchers and practitioners with a comprehensive understanding of current AI capabilities, limitations, and future trajectories in creative applications.

The abstract, and the depositor's additional notes after it when the source has a field for them.

Subject dcterms:subject

source resource/categories/0

cs.AI (arxiv)

Declared keywords and taxonomy terms, each carrying its scheme.

Language dcterms:language

language gate read the declared field

The language of the content as the language gate read it.

Audience dcterms:audience

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when the source declares an intended audience. Never inferred.

Education level dcterms:educationLevel

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when the source declares an educational context.

Rights 3/4

Licence dcterms:license

licence gate arXiv:arXiv/arXiv:license

http://creativecommons.org/licenses/by/4.0/

The licence document, as a URI where the gate resolved one.

Rights dcterms:rights

licence gate arXiv:arXiv/arXiv:license

CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive

The verdict and the conditions, in words.

Rights holder dcterms:rightsHolder

none found — the source did not declare it

Only when named at source.

Access rights dcterms:accessRights

source resource

open

The access condition the source declared. Not the licence.

Form and dates 4/10

Format dcterms:format

conversion latexml-html

text/markdown

One value per form held — the original and the extracted text.

Extent dcterms:extent

conversion latexml-html

263843 bytes (extracted text)

Sizes in bytes, each saying which form it measures.

Issued dcterms:issued

source resource

2025-01-06

When the resource was published. Not when it was collected.

Modified dcterms:modified

source resource

2026-01-23

When the source's own record was last changed.

Created dcterms:created

source resource

none found — the source did not declare it

Accepted dcterms:dateAccepted

source resource

none found — the source did not declare it

When a publisher accepted the work, where the source records it.

Submitted dcterms:dateSubmitted

source resource

none found — the source did not declare it

When the work was submitted, where the source records it.

Available dcterms:available

source resource

none found — the source did not declare it

Valid dcterms:valid

source resource

none found — the source did not declare it

Bibliographic citation dcterms:bibliographicCitation

none found — the source did not declare it

Where the work was published, in the source's own words: a journal with its volume and pages, a conference, an imprint.

Relations and custody 1/9

Is part of dcterms:isPartOf

none found — the source did not declare it

The repository, book or record this resource was found inside.

Has part dcterms:hasPart

none found — the source did not declare it

What this resource is made of, when the source lists its parts.

Is version of dcterms:isVersionOf

none found — the source did not declare it

Has version dcterms:hasVersion

none found — the source did not declare it

References dcterms:references

inferred read its reference list, line 522 citation

doi:10.30574/ijsra.2025.14.1.0061
doi:10.1016/j.eswa.2024.125952
doi:10.1007/s10462-021-10039-7
arXiv:2402.01970
doi:10.1016/j.patrec.2023.05.017
arXiv:2412.17532
arXiv:2312.02218
arXiv:2204.05862
doi:10.1109/cvpr52688.2022.00539
doi:10.1109/cvprw59228.2023.00164
arXiv:2004.10934
arXiv:2108.07258
and 143 more, every one of them in the export

Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.

Is referenced by dcterms:isReferencedBy

none found — the source did not declare it

What links to or cites this one, when a source declares it; inferred from the texts held otherwise, and set apart.

Requires dcterms:requires

none found — the source did not declare it

What this resource needs in order to be used, when a source declares it. The files a lab works on are inferred, and stand under it apart.

Is required by dcterms:isRequiredBy

none found — the source did not declare it

What needs this resource, when a source declares it. The lab a component belongs to is inferred, and stands under it apart.

Provenance dcterms:provenance

this engine source
conversion latexml-html

Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it.

Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.

IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other. the standard ↗

1 General 5/8

Identifier 1.1

this engine resource_id
source 2 assertions

URI: tag:aim-pro.eu,2026:oer/94988a1ebfff
URI: https://arxiv.org/abs/2501.02725
ARXIV: 2501.02725
DOI: 10.1007/s10462-026-11494-w

This engine's identifier for the resource, the source URI, and any persistent identifier the source published.

Title 1.2

source resource/title

Advances in Artificial Intelligence: A Review for the Creative Industries

Language 1.3

language gate read the declared field

The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.

Description 1.4

source resource/summary

Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing reviews have not comprehensively addressed these recent breakthroughs and their integrated impact across the creative production pipeline. This paper addresses this gap by providing a systematic review of AI technologies that have emerged or matured since our 2022 review, examining their applications across content creation, information analysis, post-production enhancement, compression, and quality assessment. We document how transformers, LLMs, diffusion models, and implicit neural representations have established new capabilities in text-to-image/video generation, real-time 3D reconstruction, and unified multi-task frameworks-shifting AI from support tool to core creative technology. Beyond technological advances, we analyze the trend toward unified AI frameworks that integrate multiple creative tasks, replacing task-specific solutions. We critically examine the evolving role of human-AI collaboration, where human oversight remains essential for creative direction and mitigating AI hallucinations. Finally, we identify emerging challenges including copyright concerns, bias mitigation, computational demands, and the need for robust regulatory frameworks. This review provides researchers and practitioners with a comprehensive understanding of current AI capabilities, limitations, and future trajectories in creative applications.

The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.

Keyword 1.5

source resource/categories/0

cs.AI

Coverage 1.6

not collected — this library does not fill it

The time, place or culture the resource applies to. No source declares it.

Structure 1.7

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

Aggregation level 1.8

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

2 Life cycle 3/3

Version 2.1 source resource: v5
Status 2.2 source resource: revised

Draft, final, revised or unavailable, and only when the source says so. A first version is not thereby final.
Contribute 2.3 source 3 assertions: Nantheera Anantrasirichai — author
Fan Zhang — author
David Bull — author

Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.

3 Meta-metadata 4/4

Identifier 3.1 this engine resource_id: URI: tag:aim-pro.eu,2026:oer/94988a1ebfff/record

The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute 3.2 this engine source: AIM-PRO WP3 OER harvester (arXiv) — creator

Who generated this record and when — a statement about the record, not about the resource.
Metadata schema 3.3: LOMv1.0
aimpro-oer-profile/1

LOMv1.0, and the profile this was built against.
Language 3.4 language gate read the declared field: en

4 Technical 3/7

Format 4.1

conversion latexml-html

text/markdown

One value per form held: the original as the source published it, and the Markdown this engine extracted.

Size 4.2

conversion latexml-html

263843

Bytes. The original's, because the resource is the file and not our conversion of it.

Location 4.3

this engine resource_id

https://arxiv.org/abs/2501.02725

Where the source serves it.

Requirement 4.4

not collected — this library does not fill it

Software or hardware needed to use it. No source declares it.

Installation remarks 4.5

not collected — this library does not fill it

No source declares it.

Other platform requirements 4.6

not collected — this library does not fill it

No source declares it.

Duration 4.7

not collected — this library does not fill it

Playing time, for audio and video. The corpus holds neither.

5 Educational 1/11

Interactivity type 5.1

not collected — this library does not fill it

Active, expositive or mixed. A judgement about how the resource is used; no source declares it.

Learning resource type 5.2

source resource
mapped learning_resource_type/preprint

narrative text

Only from a declaration that maps onto LOM's vocabulary. A file format is not a declaration.

Interactivity level 5.3

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Semantic density 5.4

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Intended end user role 5.5

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Context 5.6

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Typical age range 5.7

not collected — this library does not fill it

No source declares it.

Difficulty 5.8

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared and never estimated.

Typical learning time 5.9

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared. Never computed from a word count.

Description 5.10

mapped learning_resource_type/preprint

none found — the source did not declare it

An explicit statement about educational use, when the source makes one.

Language 5.11

not collected — this library does not fill it

The language of the intended learner. Not assumed to be the content's: a lesson in English may be written for learners of it.

6 Rights 3/3

Cost 6.1 licence gate arXiv:arXiv/arXiv:license: no — free to use

No: the licence gate refuses anything it cannot establish open reuse terms for, before the bytes are requested.
Copyright and other restrictions 6.2 licence gate arXiv:arXiv/arXiv:license: yes — attribution required (CC-BY-4.0)

Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
Description 6.3 licence gate arXiv:arXiv/arXiv:license: CC-BY-4.0 · http://creativecommons.org/licenses/by/4.0/. Conditions: adaptation permitted, attribution required.

The SPDX id, the licence URI and the conditions. LOM has no element for any of the three, so this is where they survive.

7 Relation 0/2

Kind 7.1: none found — the source did not declare it
Resource 7.2 inferred read its reference list, line 522 citation: references: doi:10.30574/ijsra.2025.14.1.0061
references: doi:10.1016/j.eswa.2024.125952
references: doi:10.1007/s10462-021-10039-7
references: arXiv:2402.01970
references: doi:10.1016/j.patrec.2023.05.017
references: arXiv:2412.17532
references: arXiv:2312.02218
references: arXiv:2204.05862
references: doi:10.1109/cvpr52688.2022.00539
references: doi:10.1109/cvprw59228.2023.00164
references: arXiv:2004.10934
references: arXiv:2108.07258
and 143 more, every one of them in the export

The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.

8 Annotation 0/3

Entity 8.1

not collected — this library does not fill it

Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.

Date 8.2

not collected — this library does not fill it

Description 8.3

not collected — this library does not fill it

9 Classification 0/4

Purpose 9.1

not collected — this library does not fill it

Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.

Taxon path 9.2

not collected — this library does not fill it

Where the competency framework goes. Empty in the record for the reason above.

Description 9.3

not collected — this library does not fill it

Keyword 9.4

not collected — this library does not fill it

What could not be established3

Where the source's metadata could not be carried over as it was — missing, contradictory, with no matching term in the standard, restructured, or taken from the repository — and what was done instead. Without these notes, an empty element would look like something the harvester missed.

Status	Field	Why
Not available	`rights_holder`	no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available	`publisher`	the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available	`educational`	the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them