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SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation.

, , , , , , , and . CVPR, page 21339-21350. IEEE, (2022)

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No One Left Behind: Real-World Federated Class-Incremental Learning., , , , , and . CoRR, (2023)ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers., , , , , , and . CoRR, (2022)RMM: Reinforced Memory Management for Class-Incremental Learning., , and . CoRR, (2023)UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation., , , , , , and . ICCV, page 6769-6779. IEEE, (2023)Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation., , , , and . WACV, page 5967-5976. IEEE, (2023)Future Moment Assessment for Action Query., , and . WACV, page 3218-3227. IEEE, (2021)Towards Robust Object Detection Invariant to Real-World Domain Shifts., , , , , , and . ICLR, OpenReview.net, (2023)Shading cues for object class detection., , and . ICCV Workshops, page 649-656. IEEE Computer Society, (2009)An Ensemble of Epoch-Wise Empirical Bayes for Few-Shot Learning., , and . ECCV (16), volume 12361 of Lecture Notes in Computer Science, page 404-421. Springer, (2020)Mnemonics Training: Multi-Class Incremental Learning Without Forgetting., , , , and . CVPR, page 12242-12251. Computer Vision Foundation / IEEE, (2020)