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Video Object Segmentation without Temporal Information., , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 41 (6): 1515-1530 (2019)DeVIS: Making Deformable Transformers Work for Video Instance Segmentation., , , and . CoRR, (2022)Learn to Predict Sets Using Feed-Forward Neural Networks., , , , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (12): 9011-9025 (2022)TrackFormer: Multi-Object Tracking with Transformers., , , and . CoRR, (2021)Deep Shells: Unsupervised Shape Correspondence with Optimal Transport., , , and . NeurIPS, (2020)STEP: Segmenting and Tracking Every Pixel., , , , , , , , , and 3 other author(s). NeurIPS Datasets and Benchmarks, (2021)MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?, , , , , , , , and . ICCV, page 10829-10839. IEEE, (2021)How to Train Your Deep Multi-Object Tracker., , , , , and . CVPR, page 6786-6795. Computer Vision Foundation / IEEE, (2020)Staged Contact-Aware Global Human Motion Forecasting., , , , , and . BMVC, page 589-594. BMVA Press, (2023)The Group Loss for Deep Metric Learning., , , , and . ECCV (7), volume 12352 of Lecture Notes in Computer Science, page 277-294. Springer, (2020)