Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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,RecurrentBEV: A Long-Term Temporal Fusion Framework for?Multi-view 3D Detection, fusion ability while still enjoying efficient inference latency and memory consumption during inference. Extensive experiments on the nuScenes benchmark demonstrate its effectiveness, achieving a new state-of-the-art performance of 57.4. mAP and 65.1. NDS on the test set. The real-time version (25.

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,Straightforward Layer-Wise Pruning for?More Efficient Visual Adaptation,dimensional space obtained through ch1tspsSNE, SLS facilitates informed pruning decisions. Our study reveals that layer-wise pruning, with a focus on storing pruning indices, addresses storage volume concerns. Notably, mainstream Layer-wise pruning methods may not be suitable for assessing layer imp

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,Domain Shifting: A Generalized Solution for?Heterogeneous Cross-Modality Person Re-Identification,lities. Further, a domain alignment loss is developed to alleviate the cross-modality discrepancies by aligning the patterns across modalities. In addition, a domain distillation loss is designed to distill identity-invariant knowledge by learning the distribution of different modalities. Extensive

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,Self-Supervised Video Desmoking for?Laparoscopic Surgery,zation term are presented to avoid trivial solutions. In addition, we construct a real surgery video dataset for desmoking, which covers a variety of smoky scenes. Extensive experiments on the dataset show that our SelfSVD can remove smoke more effectively and efficiently while recovering more photo

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,Removing Rows and?Columns of?Tokens in?Vision Transformer Enables Faster Dense Prediction Without Rsed fusion method with faster speed and demonstrates higher potential in terms of robustness. Our method was applied to Segmenter, MaskDINO and SWAG, exhibiting promising performance on four tasks, including semantic segmentation, instance segmentation, panoptic segmentation, and image classificatio