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This work aims to address an advanced keypoint detection problem: how to accurately detect any keypoints in complex real-world scenarios, which involves massive, messy, and open-ended objects as well as their associated keypoints definitions.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
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Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: Database and results. Image and vision computing 47
2016
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Wu, J., Xue, T., Lim, J.J., Tian, Y., Tenenbaum, J.B., Torralba, A., Freeman, W.T.: Single image 3d interpreter network. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI 14. pp. 365–382. Springer (2016)
2016
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. pp. 1126–1135. PMLR (2017)
2017
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Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Mathis, A., Mamidanna, P., Cury, K.M., Abe, T., Murthy, V.N., Mathis, M.W., Bethge, M.: Deeplabcut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience (2018), https://www.nature.com/articles/s41593-018-0209-y
2018
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Reddy, N.D., Vo, M., Narasimhan, S.G.: Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1906–1915 (2018)
2018
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Wang, Y., Peng, C., Liu, Y.: Mask-pose cascaded cnn for 2d hand pose estimation from single color image. IEEE Transactions on Circuits and Systems for Video Technology 29
2018
Earlier work this paper cites.
Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Proceedings of the European conference on computer vision (ECCV). pp. 466–481 (2018)
2018
Earlier work this paper cites.
Cao, J., Tang, H., Fang, H.S., Shen, X., Lu, C., Tai, Y.W.: Cross-domain adaptation for animal pose estimation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9498–9507 (2019)
2019
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Ge, Y., Zhang, R., Wang, X., Tang, X., Luo, P.: Deepfashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5337–5345 (2019)
2019
Earlier work this paper cites.
Graving, J.M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B.R., Couzin, I.D.: Deepposekit, a software toolkit for fast and robust animal pose estimation using deep learning. Elife 8
2019
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2019
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Pereira, T.D., Aldarondo, D.E., Willmore, L., Kislin, M., Wang, S.S.H., Murthy, M., Shaevitz, J.W.: Fast animal pose estimation using deep neural networks. Nature methods 16
2019
Earlier work this paper cites.
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 658–666 (2019)
2019
Earlier work this paper cites.
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5693–5703 (2019)
2019
Earlier work this paper cites.
Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L.: Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5386–5395 (2020)
2020
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Khan, M.H., McDonagh, J., Khan, S., Shahabuddin, M., Arora, A., Khan, F.S., Shao, L., Tzimiropoulos, G.: Animalweb: A large-scale hierarchical dataset of annotated animal faces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6939–6948 (2020)
2020
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2020
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Ge, Y., Zhang, R., Luo, P.: Metacloth: Learning unseen tasks of dense fashion landmark detection from a few samples. IEEE Transactions on Image Processing 31
2021
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2021
Earlier work this paper cites.
Labuguen, R., Matsumoto, J., Negrete, S.B., Nishimaru, H., Nishijo, H., Takada, M., Go, Y., Inoue, K.i., Shibata, T.: Macaquepose: a novel “in the wild” macaque monkey pose dataset for markerless motion capture. Frontiers in behavioral neuroscience 14
2021
Earlier work this paper cites.
Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., Tu, Z.: Pose recognition with cascade transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1944–1953 (2021)
2021
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
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2023
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2023
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Ju, X., Zeng, A., Wang, J., Xu, Q., Zhang, L.: Human-art: A versatile human-centric dataset bridging natural and artificial scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 618–629 (2023)
2023
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4015–4026 (2023)
2023
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2021
Cited alongside, same era.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 16000–16009 (2022)
2022
Cited alongside, same era.
Lauer, J., Zhou, M., Ye, S., Menegas, W., Schneider, S., Nath, T., Rahman, M.M., Di Santo, V., Soberanes, D., Feng, G., et al.: Multi-animal pose estimation, identification and tracking with deeplabcut. Nature Methods 19
2022
Cited alongside, same era.
Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J.N., et al.: Grounded language-image pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10965–10975 (2022)
2022
Cited alongside, same era.
Lu, C., Koniusz, P.: Few-shot keypoint detection with uncertainty learning for unseen species. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19416–19426 (2022)
2022
Cited alongside, same era.
Mao, W., Ge, Y., Shen, C., Tian, Z., Wang, X., Wang, Z., den Hengel, A.v.: Poseur: Direct human pose regression with transformers. In: European Conference on Computer Vision. pp. 72–88. Springer (2022)
2022
Cited alongside, same era.
Ng, X.L., Ong, K.E., Zheng, Q., Ni, Y., Yeo, S.Y., Liu, J.: Animal kingdom: A large and diverse dataset for animal behavior understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19023–19034 (2022)
2022
Cited alongside, same era.
Shi, D., Wei, X., Li, L., Ren, Y., Tan, W.: End-to-end multi-person pose estimation with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11069–11078 (2022)
2022
Cited alongside, same era.
Xu, L., Jin, S., Zeng, W., Liu, W., Qian, C., Ouyang, W., Luo, P., Wang, X.: Pose for everything: Towards category-agnostic pose estimation. In: European Conference on Computer Vision. pp. 398–416. Springer (2022)
2022
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2023
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Liang, F., Wu, B., Dai, X., Li, K., Zhao, Y., Zhang, H., Zhang, P., Vajda, P., Marculescu, D.: Open-vocabulary semantic segmentation with mask-adapted clip. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7061–7070 (2023)
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Shi, M., Huang, Z., Ma, X., Hu, X., Cao, Z.: Matching is not enough: A two-stage framework for category-agnostic pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7308–7317 (2023)
2023
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Yang, J., Wang, C., Li, Z., Wang, J., Zhang, R.: Semantic human parsing via scalable semantic transfer over multiple label domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19424–19433 (2023)
2023
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2023
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Zhang, X., Wang, W., Chen, Z., Xu, Y., Zhang, J., Tao, D.: Clamp: Prompt-based contrastive learning for connecting language and animal pose. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23272–23281 (2023)
2023
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Zhou, M., Stoffl, L., Mathis, M.W., Mathis, A.: Rethinking pose estimation in crowds: Overcoming the detection information bottleneck and ambiguity. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 14689–14699 (October 2023)
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2024
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Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et al.: Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 24185–24198 (2024)
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Wang, W., Chen, Z., Chen, X., Wu, J., Zhu, X., Zeng, G., Luo, P., Lu, T., Zhou, J., Qiao, Y., et al.: Visionllm: Large language model is also an open-ended decoder for vision-centric tasks. Advances in Neural Information Processing Systems 36
2024
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Yang, J., Li, B., Zeng, A., Zhang, L., Zhang, R.: Open-world human-object interaction detection via multi-modal prompts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16954–16964 (2024)
2024
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Zou, X., Yang, J., Zhang, H., Li, F., Li, L., Wang, J., Wang, L., Gao, J., Lee, Y.J.: Segment everything everywhere all at once. Advances in Neural Information Processing Systems 36
2024
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