A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering

J. R. Hobbs, “Granularity,” in Readings in qualitative reasoning about physical systems . Elsevier, 1990, pp. 542–545

1990

Earlier work this paper cites.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013

2013

Earlier work this paper cites.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013

Original

2013

Earlier work this paper cites.

S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 567–576

2015

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in ICLR , 2015

2015

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS , 2017

2017

Earlier work this paper cites.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2017

2017

Earlier work this paper cites.

A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5828–5839

2017

Earlier work this paper cites.

R. Arandjelovic and A. Zisserman, “Look, listen and learn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 609–617

2017

Earlier work this paper cites.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” in ICLR , 2017

2017

Earlier work this paper cites.

X. Liang, K. Gong, X. Shen, and L. Lin, “Look into person: Joint body parsing & pose estimation network and a new benchmark,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 4, pp. 871–885, 2018

2018

Earlier work this paper cites.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in ICLR , 2018

2018

Earlier work this paper cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018

Original

2018

Earlier work this paper cites.

P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, “Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 9592–9600

2019

Earlier work this paper cites.

N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European conference on computer vision . Springer, 2020, pp. 213–229

2020

Earlier work this paper cites.

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

Original

2020

Earlier work this paper cites.

U. Michieli, E. Borsato, L. Rossi, and P. Zanuttigh, “Gmnet: Graph matching network for large scale part semantic segmentation in the wild,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VIII 16 . Springer, 2020, pp. 397–414

2020

Earlier work this paper cites.

H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631

2020

Earlier work this paper cites.

M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan et al. , “The prisma 2020 statement: an updated guideline for reporting systematic reviews,” bmj , vol. 372, 2021

2021

Earlier work this paper cites.

B. Cheng, A. Schwing, and A. Kirillov, “Per-pixel classification is not all you need for semantic segmentation,” Advances in neural information processing systems , vol. 34, pp. 17 864–17 875, 2021

2021

Earlier work this paper cites.

X. Li, S. Xu, Y. Yang, G. Cheng, Y. Tong, and D. Tao, “Panoptic-partformer: Learning a unified model for panoptic part segmentation,” in European Conference on Computer Vision . Springer, 2022, pp. 729–747

2022

Earlier work this paper cites.

H. K. Cheng and A. G. Schwing, “Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model,” 2022

2022

Earlier work this paper cites.

J. Zhou, J. Wang, J. Zhang, W. Sun, J. Zhang, S. Birchfield, D. Guo, L. Kong, M. Wang, and Y. Zhong, “Audio–visual segmentation,” in European Conference on Computer Vision . Springer, 2022, pp. 386–403

2022

Earlier work this paper cites.

Y. Zou, J. Jeong, L. Pemula, D. Zhang, and O. Dabeer, “Spot-the-difference self-supervised pre-training for anomaly detection and segmentation,” in European Conference on Computer Vision . Springer, 2022, pp. 392–408

2022

Earlier work this paper cites.

A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo et al. , “Segment anything,” arXiv preprint arXiv:2304.02643 , 2023

Original

2023

Earlier work this paper cites.

C. Zhang, L. Liu, Y. Cui, G. Huang, W. Lin, Y. Yang, and Y. Hu, “A comprehensive survey on segment anything model for vision and beyond,” arXiv preprint arXiv:2305.08196 , 2023

Original

2023

Earlier work this paper cites.

Y. Zhang and R. Jiao, “Towards segment anything model (sam) for medical image segmentation: a survey,” arXiv preprint arXiv:2305.03678 , 2023

Original

2023

Earlier work this paper cites.

Z. Wang, W. Wei, Y. Zhao, Z. Xiao, M. A. Hasegawa-Johnson, H. Shi, and T. Hou, “Hifi tuner: High-fidelity subject-driven fine-tuning for diffusion models,” ArXiv , vol. abs/2312.00079, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265551490

Original

2023

Earlier work this paper cites.

S. F. Bhat, N. J. Mitra, and P. Wonka, “Loosecontrol: Lifting controlnet for generalized depth conditioning,” ArXiv , vol. abs/2312.03079, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265693942

Original

2023

Earlier work this paper cites.

Y. Feng, B. Gong, D. Chen, Y. Shen, Y. Liu, and J. Zhou, “Ranni: Taming text-to-image diffusion for accurate instruction following,” ArXiv , vol. abs/2311.17002, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265466135

Original

2023

Earlier work this paper cites.

Z. Wang, Z. Sha, Z. Ding, Y. Wang, and Z. Tu, “Tokencompose: Grounding diffusion with token-level supervision,” ArXiv , vol. abs/2312.03626, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265723245

Original

2023

Earlier work this paper cites.

T. Yu, R. Feng, R. Feng, J. Liu, X. Jin, W. Zeng, and Z. Chen, “Inpaint anything: Segment anything meets image inpainting,” arXiv preprint arXiv:2304.06790 , 2023

Original

2023

Earlier work this paper cites.

J. Yao, X. Wang, L. Ye, and W. Liu, “Matte anything: Interactive natural image matting with segment anything models,” arXiv preprint arXiv:2306.04121 , 2023

Original

2023

Earlier work this paper cites.

L. Yang, Y. Wang, X. Li, X. Wang, and J. Yang, “Fine-grained visual prompting,” arXiv preprint arXiv:2306.04356 , 2023

Original

2023

Earlier work this paper cites.

P. Ganugula, Y. Kumar, N. K. Reddy, P. Chellingi, A. K. Thakur, N. Kasera, and C. S. Anand, “Mosaic: Multi-object segmented arbitrary stylization using clip,” 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) , pp. 892–903, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:262464521

2023

Earlier work this paper cites.

V. Vavilala and D. A. Forsyth, “Applying a color palette with local control using diffusion models,” ArXiv , vol. abs/2307.02698, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259360646

Original

2023

Earlier work this paper cites.

J. Jiang and C. Holz, “Restore anything pipeline: Segment anything meets image restoration,” ArXiv , vol. abs/2305.13093, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258833181

Original

2023

Earlier work this paper cites.

Z. Xiao, J. Bai, Z. Lu, and Z. Xiong, “A dive into sam prior in image restoration,” ArXiv , vol. abs/2305.13620, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258841728

Original

2023

Earlier work this paper cites.

T. Chen, Z. Mai, R. Li, and W.-L. Chao, “Segment anything model (sam) enhanced pseudo labels for weakly supervised semantic segmentation,” ArXiv , vol. abs/2305.05803, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258587943

Original

2023

Earlier work this paper cites.

P.-T. Jiang and Y. Yang, “Segment anything is a good pseudo-label generator for weakly supervised semantic segmentation,” ArXiv , vol. abs/2305.01275, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258436872

Original

2023

Earlier work this paper cites.

X. Chen, Y. Jia, and Y. Wu, “Fine-grained annotation for face anti-spoofing,” ArXiv , vol. abs/2310.08142, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:263909098

Original

2023

Earlier work this paper cites.

J. Wu, R. Xu, Z. Wood-Doughty, and C. Wang, “Segment anything model is a good teacher for local feature learning,” ArXiv , vol. abs/2309.16992, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:263310722

Original

2023

Earlier work this paper cites.

R. Zhang, Z. Jiang, Z. Guo, S. Yan, J. Pan, H. Dong, P. Gao, and H. Li, “Personalize segment anything model with one shot,” ArXiv , vol. abs/2305.03048, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258480276

Original

2023

Earlier work this paper cites.

Y.-C. Lee, Z. Zhang, K. Blackburn-Matzen, S. Niklaus, J. Zhang, J.-B. Huang, and F. Liu, “Fast view synthesis of casual videos,” ArXiv , vol. abs/2312.02135, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265609797

Original

2023

Earlier work this paper cites.

X. Shen and M. Elhoseiny, “Storygpt-v: Large language models as consistent story visualizers,” ArXiv , vol. abs/2312.02252, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265658856

Original

2023

Earlier work this paper cites.

B. Qin, W. Ye, Q. Yu, S. Tang, and Y. Zhuang, “Dancing avatar: Pose and text-guided human motion videos synthesis with image diffusion model,” ArXiv , vol. abs/2308.07749, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:260900206

Original

2023

Earlier work this paper cites.

T. Delatolas, V. S. Kalogeiton, and D. P. Papadopoulos, “Learning the what and how of annotation in video object segmentation,” 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pp. 6936–6946, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265050493

2023

Earlier work this paper cites.

H. He, J. Zhang, M. Xu, J. Liu, B. Du, and D. Tao, “Scalable mask annotation for video text spotting,” ArXiv , vol. abs/2305.01443, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258436950

Original

2023

Earlier work this paper cites.

J. Yang, M. Gao, Z. Li, S. Gao, F. Wang, and F. Zheng, “Track anything: Segment anything meets videos,” arXiv preprint arXiv:2304.11968 , 2023

Original

2023

Earlier work this paper cites.

D. Zhang, C. Li, R. Zhang, S. Xie, W. Xue, X. Xie, and S. Zhang, “Fm-ov3d: Foundation model-based cross-modal knowledge blending for open-vocabulary 3d detection,” in AAAI Conference on Artificial Intelligence , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:266520928

2023

Earlier work this paper cites.

IDEA-Research, “Grounded segment anything,” 2023, gitHub repository. [Online]. Available: https://github.com/IDEA-Research/Grounded-Segment-Anything

2023

Earlier work this paper cites.

L. Kong, Y. Liu, X. Li, R. Chen, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robo3d: Towards robust and reliable 3d perception against corruptions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 994–20 006

2023

Earlier work this paper cites.

H. Zhang, F. Li, and N. Ahuja, “Open-nerf: Towards open vocabulary nerf decomposition,” 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pp. 3444–3453, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:264451574

2023

Earlier work this paper cites.

Y. Wang, W. Liu, G. Li, J. Ding, D. Hu, and X. Li, “Prompting segmentation with sound is generalizable audio-visual source localizer,” ArXiv , vol. abs/2309.07929, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:261881829

Original

2023

Earlier work this paper cites.

J. Liu, Y. Wang, C. Ju, C. Ma, Y. Zhang, and W. Xie, “Annotation-free audio-visual segmentation,” 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pp. 5592–5602, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258762555

2023

Earlier work this paper cites.

S. Bhosale, H. Yang, D. Kanojia, and X. Zhu, “Leveraging foundation models for unsupervised audio-visual segmentation,” ArXiv , vol. abs/2309.06728, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:261705977

Original

2023

Earlier work this paper cites.

S. Mo and Y. Tian, “Av-sam: Segment anything model meets audio-visual localization and segmentation,” ArXiv , vol. abs/2305.01836, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258461460

Original

2023

Earlier work this paper cites.

T. Ohkawa, T. Yagi, T. Nishimura, R. Furuta, A. Hashimoto, Y. Ushiku, and Y. Sato, “Exo2egodvc: Dense video captioning of egocentric procedural activities using web instructional videos,” ArXiv , vol. abs/2311.16444, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265466768

Original

2023

Earlier work this paper cites.

X. Huang, J. Wang, Y. Tang, Z. Zhang, H. Hu, J. Lu, L. Wang, and Z. Liu, “Segment and caption anything,” ArXiv , vol. abs/2312.00869, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265609446

Original

2023

Earlier work this paper cites.

T. Wang, J. Zhang, J. Fei, Y. Ge, H. Zheng, Y. Tang, Z. Li, M. Gao, S. Zhao, Y. Shan, and F. Zheng, “Caption anything: Interactive image description with diverse multimodal controls,” ArXiv , vol. abs/2305.02677, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258479994

Original

2023

Earlier work this paper cites.

Q. Zhou, C. Yu, S. Zhang, S. Wu, Z. Wang, and F. Wang, “Regionblip: A unified multi-modal pre-training framework for holistic and regional comprehension,” ArXiv , vol. abs/2308.02299, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:260611353

Original

2023

Earlier work this paper cites.

R. Chen, Y.-C. Liu, L. Kong, N. Chen, X. Zhu, Y. Ma, T. Liu, and W. Wang, “Towards label-free scene understanding by vision foundation models,” ArXiv , vol. abs/2306.03899, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259088858

Original

2023

Earlier work this paper cites.

S. Doveh, A. Arbelle, S. Harary, R. Herzig, D. Kim, P. Cascante-Bonilla, A. Alfassy, R. Panda, R. Giryes, R. S. Feris, S. Ullman, and L. Karlinsky, “Dense and aligned captions (dac) promote compositional reasoning in vl models,” ArXiv , vol. abs/2305.19595, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258987899

Original

2023

Earlier work this paper cites.

Z. Sun, Y. Fang, T. Wu, P. Zhang, Y. Zang, S. Kong, Y. Xiong, D. Lin, and J. Wang, “Alpha-clip: A clip model focusing on wherever you want,” ArXiv , vol. abs/2312.03818, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:266055413

Original

2023

Earlier work this paper cites.

Y. Yang, C. Neary, and U. Topcu, “Multimodal pretrained models for verifiable sequential decision-making: Planning, grounding, and perception,” in Adaptive Agents and Multi-Agent Systems , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:260775783

2023

Earlier work this paper cites.

Z. Li, B. Yang, Q. Liu, Z. Ma, S. Zhang, J. Yang, Y. Sun, Y. Liu, and X. Bai, “Monkey: Image resolution and text label are important things for large multi-modal models,” ArXiv , vol. abs/2311.06607, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265150038

Original

2023

Earlier work this paper cites.

J. Xu, L. Xu, Y. Yang, X. Li, Y. Xie, Y. Huang, and Y. Li, “u-llava: Unifying multi-modal tasks via large language model,” ArXiv , vol. abs/2311.05348, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265067051

Original

2023

Earlier work this paper cites.

S. Liu, H. Cheng, H. Liu, H. Zhang, F. Li, T. Ren, X. Zou, J. Yang, H. Su, J.-J. Zhu, L. Zhang, J. Gao, and C. yue Li, “Llava-plus: Learning to use tools for creating multimodal agents,” ArXiv , vol. abs/2311.05437, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265067489

Original

2023

Earlier work this paper cites.

M. Cai, Z. Huang, Y. Li, H. Wang, and Y. J. Lee, “Leveraging large language models for scalable vector graphics-driven image understanding,” ArXiv , vol. abs/2306.06094, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259129535

Original

2023

Earlier work this paper cites.

C.-Y. Hsieh, S. Chen, C.-L. Li, Y. Fujii, A. J. Ratner, C.-Y. Lee, R. Krishna, and T. Pfister, “Tool documentation enables zero-shot tool-usage with large language models,” ArXiv , vol. abs/2308.00675, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:260351459

Original

2023

Earlier work this paper cites.

Y. Yuan, W. Li, J. Liu, D. Tang, X. Luo, C. Qin, L. Zhang, and J. Zhu, “Osprey: Pixel understanding with visual instruction tuning,” ArXiv , vol. abs/2312.10032, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:266335219

Original

2023

Earlier work this paper cites.

B. Xiao, H. Wu, W. Xu, X. Dai, H. Hu, Y. Lu, M. Zeng, C. Liu, and L. Yuan, “Florence-2: Advancing a unified representation for a variety of vision tasks,” ArXiv , vol. abs/2311.06242, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265128818

Original

2023

Earlier work this paper cites.

Z. Liu, Y. He, W. Wang, W. Wang, Y. Wang, S. Chen, Q.-L. Zhang, Y. Yang, Q. Li, J. Yu, K. Li, Z. Chen, X. Yang, X. Zhu, Y. Wang, L. Wang, P. Luo, J. Dai, and Y. Qiao, “Interngpt: Solving vision-centric tasks by interacting with chatbots beyond language,” ArXiv , vol. abs/2305.05662, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258564914

Original

2023

Earlier work this paper cites.

J. Xu, X. Zhou, S. Yan, X. Gu, A. Arnab, C. Sun, X. Wang, and C. Schmid, “Pixel aligned language models,” ArXiv , vol. abs/2312.09237, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:266209999

Original

2023

Earlier work this paper cites.

H. A. Rasheed, M. Maaz, S. S. Mullappilly, A. M. Shaker, S. H. Khan, H. Cholakkal, R. M. Anwer, E. Xing, M.-H. Yang, and F. S. Khan, “Glamm: Pixel grounding large multimodal model,” ArXiv , vol. abs/2311.03356, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265043538

Original

2023

Cited alongside, same era.

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,” ArXiv , vol. abs/2312.06109, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:266163175

Original

2023

Cited alongside, same era.

Y. Zhao, Z. Lin, D. Zhou, Z. Huang, J. Feng, and B. Kang, “Bubogpt: Enabling visual grounding in multi-modal llms,” ArXiv , vol. abs/2307.08581, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259937702

Original

2023

Cited alongside, same era.

Z. Chen, Y. Zhu, Y. Zhan, Z. Li, C. Zhao, J. Wang, and M. Tang, “Mitigating hallucination in visual language models with visual supervision,” ArXiv , vol. abs/2311.16479, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:265466729

Original

W. Li, X. Xu, J. Liu, and X. Xiao, “Unimo-g: Unified image generation through multimodal conditional diffusion,” ArXiv , vol. abs/2401.13388, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:267200040

Original

2024

Closest in time.

M. Gain, A. D. Raha, and R. Debnath, “Ccc++: Optimized color classified colorization with segment anything model (sam) empowered object selective color harmonization,” ArXiv , vol. abs/2403.11494, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:268512894

Original

2024

Closest in time.

C. Wang, Z. Hao, Y. Tang, J. Guo, Y. Yang, K. Han, and Y. Wang, “Sam-diffsr: Structure-modulated diffusion model for image super-resolution,” ArXiv , vol. abs/2402.17133, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:268031901

Original

2024

Closest in time.

J. Xue, T. Wang, J. Wang, K. Zhang, W. Luo, W. Ren, Z. Liu, H. Park, and X. Cao, “Segmentation guided sparse transformer for under-display camera image restoration,” ArXiv , vol. abs/2403.05906, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:268357044

Original