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InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting.

, , , , , and . ICCV, page 682-691. IEEE, (2019)

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Learning deep graph matching with channel-independent embedding and Hungarian attention., , , and . ICLR, OpenReview.net, (2020)Combinatorial Learning of Graph Edit Distance via Dynamic Embedding., , , , and . CVPR, page 5241-5250. Computer Vision Foundation / IEEE, (2021)MixSATGEN: Learning Graph Mixing for SAT Instance Generation., , , and . ICLR, OpenReview.net, (2024)Pygmtools: A Python Graph Matching Toolkit., , , , , , , , , and 3 other author(s). J. Mach. Learn. Res., (2024)Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions., , , , , , , and . WWW, page 3173-3184. ACM, (2024)InstaBoost++: Visual Coherence Principles for Unified 2D/3D Instance Level Data Augmentation., , , , , and . Int. J. Comput. Vis., 131 (10): 2665-2681 (October 2023)GMTR: Graph Matching Transformers., , , , and . CoRR, (2023)Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond., , , and . CVPR, page 15242-15251. IEEE, (2022)Learning Combinatorial Embedding Networks for Deep Graph Matching., , and . ICCV, page 3056-3065. IEEE, (2019)InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting., , , , , and . ICCV, page 682-691. IEEE, (2019)