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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Panacea

https://doi.org/10.1007/978-3-031-43461-7the-art performance in the large vocabulary?LVIS dataset with different backbones and architectures. It generalizes well to more difficult evaluation metrics, relatively balanced datasets, and the mask branch. This is the first attempt to reveal and explore rectifying of the regression bias in long-

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recession

https://doi.org/10.1007/978-3-658-17433-0stency for each modality. After filtering out ST voxels?with high ST entropy, Latte conducts cross-modal learning for each?point and pixel by attending to those with reliable and consistent predictions among both spatial and temporal neighborhoods. Experimental results show that Latte achieves state

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