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Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes.
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T. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in ICCV , 2017
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L. Gao, B. Wang, and W. Wang, “Image captioning with scene-graph based semantic concepts,” in ICMLC , 2018
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J. Yang, J. Lu, S. Lee, D. Batra, and D. Parikh, “Graph R-CNN for scene graph generation,” in ECCV (1) , 2018
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J. Zhang, K. J. Shih, A. Elgammal, A. Tao, and B. Catanzaro, “Graphical contrastive losses for scene graph parsing,” in CVPR , 2019
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A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in CVPR , 2019
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A. Kirillov, R. B. Girshick, K. He, and P. Dollár, “Panoptic feature pyramid networks,” in CVPR , 2019
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Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, and R. Urtasun, “Upsnet: A unified panoptic segmentation network,” in CVPR , 2019
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T. Chen, W. Yu, R. Chen, and L. Lin, “Knowledge-embedded routing network for scene graph generation,” in CVPR , 2019
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I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in ICLR (Poster) , 2019
2019
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S. Chen, Q. Jin, P. Wang, and Q. Wu, “Say as you wish: Fine-grained control of image caption generation with abstract scene graphs,” in CVPR , 2020
2020
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Y. Zhong, L. Wang, J. Chen, D. Yu, and Y. Li, “Comprehensive image captioning via scene graph decomposition,” in ECCV (14) , 2020
2020
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X. Lin, C. Ding, J. Zeng, and D. Tao, “Gps-net: Graph property sensing network for scene graph generation,” in CVPR , 2020
2020
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K. Tang, Y. Niu, J. Huang, J. Shi, and H. Zhang, “Unbiased scene graph generation from biased training,” in CVPR , 2020
2020
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B. Kim, J. Lee, J. Kang, E. Kim, and H. J. Kim, “HOTR: end-to-end human-object interaction detection with transformers,” in CVPR , 2021
2021
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2021
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021
2021
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2021
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B. Cheng, M. D. Collins, Y. Zhu, T. Liu, T. S. Huang, H. Adam, and L. Chen, “Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation,” in CVPR , 2020
2020
Cited alongside, same era.
Y. Wu, G. Zhang, H. Xu, X. Liang, and L. Lin, “Auto-panoptic: Cooperative multi-component architecture search for panoptic segmentation,” in NeurIPS , 2020
2020
Cited alongside, same era.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in ECCV , 2020
2020
Cited alongside, same era.
P. Sun, Y. Jiang, R. Zhang, E. Xie, J. Cao, X. Hu, T. Kong, Z. Yuan, C. Wang, and P. Luo, “Transtrack: Multiple-object tracking with transformer,” CoRR , 2020
2020
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A. Kuznetsova, H. Rom, N. Alldrin, J. R. R. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, T. Duerig, and V. Ferrari, “The open images dataset V4,” Int. J. Comput. Vis. , 2020
2020
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R. Li, S. Zhang, B. Wan, and X. He, “Bipartite graph network with adaptive message passing for unbiased scene graph generation,” in CVPR , 2021
2021
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Q. Dong, Z. Tu, H. Liao, Y. Zhang, V. Mahadevan, and S. Soatto, “Visual relationship detection using part-and-sum transformers with composite queries,” in ICCV , 2021
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in ICCV , 2021
2021
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J. Yang, Y. Z. Ang, Z. Guo, K. Zhou, W. Zhang, and Z. Liu, “Panoptic scene graph generation,” in ECCV (27) , 2022
2022
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R. Li, S. Zhang, and X. He, “SGTR: end-to-end scene graph generation with transformer,” in CVPR , 2022
2022
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Y. Teng and L. Wang, “Structured sparse R-CNN for direct scene graph generation,” in CVPR , 2022
2022
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X. Li, W. Zhang, J. Pang, K. Chen, G. Cheng, Y. Tong, and C. C. Loy, “Video k-net: A simple, strong, and unified baseline for video segmentation,” in CVPR , 2022
2022
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L. Xu, H. Qu, J. Kuen, J. Gu, and J. Liu, “Meta spatio-temporal debiasing for video scene graph generation,” in ECCV (27) , 2022
2022
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K. Gao, L. Chen, Y. Niu, J. Shao, and J. Xiao, “Classification-then-grounding: Reformulating video scene graphs as temporal bipartite graphs,” in CVPR , 2022
2022
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B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in CVPR , 2022
2022
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H. Yuan, X. Li, Y. Yang, G. Cheng, J. Zhang, Y. Tong, L. Zhang, and D. Tao, “Polyphonicformer: Unified query learning for depth-aware video panoptic segmentation,” in ECCV , 2022
2022
Later among the works it cites.
F. Zeng, B. Dong, Y. Zhang, T. Wang, X. Zhang, and Y. Wei, “MOTR: end-to-end multiple-object tracking with transformer,” in ECCV (27) , 2022
2022
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2022
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2022
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R. Ferjaoui, M. A. Cherni, F. Abidi, and A. Zidi, “Deep residual learning based on resnet50 for COVID-19 recognition in lung CT images,” in CoDIT , 2022
2022
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J. Yang, W. Peng, X. Li, Z. Guo, L. Chen, B. Li, Z. Ma, K. Zhou, W. Zhang, C. C. Loy et al. , “Panoptic video scene graph generation,” in CVPR , 2023
2023
Closest in time.
Y. Li, H. Zhao, X. Qi, Y. Chen, L. Qi, L. Wang, Z. Li, J. Sun, and J. Jia, “Fully convolutional networks for panoptic segmentation with point-based supervision,” IEEE Trans. Pattern Anal. Mach. Intell. , 2023
2023
Closest in time.
X. Li, S. Xu, Y. Yang, H. Yuan, G. Cheng, Y. Tong, Z. Lin, and D. Tao, “Panopticpartformer++: A unified and decoupled view for panoptic part segmentation,” CoRR , 2023
2023
Closest in time.
X. Li, H. Yuan, W. Zhang, G. Cheng, J. Pang, and C. C. Loy, “Tube-link: A flexible cross tube baseline for universal video segmentation,” ICCV , 2023
2023
Closest in time.
Y. Han, J. Zhang, Z. Xue, C. Xu, X. Shen, Y. Wang, C. Wang, Y. Liu, and X. Li, “Reference twice: A simple and unified baseline for few-shot instance segmentation,” arxiv , 2023
2023
Closest in time.
K. Li, Z. Yang, L. Chen, Y. Yang, and J. Xiao, “Catr: Combinatorial-dependence audio-queried transformer for audio-visual video segmentation,” in ACM-MM , 2023
2023
Closest in time.
Y. Xu, Z. Yang, and Y. Yang, “Video object segmentation in panoptic wild scenes,” in IJCAI , 2023
2023
Closest in time.
X. Chang, P. Ren, P. Xu, Z. Li, X. Chen, and A. Hauptmann, “A comprehensive survey of scene graphs: Generation and application,” IEEE Trans. Pattern Anal. Mach. Intell. , 2023
2023
Closest in time.