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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hermitage

ONYM

https://doi.org/10.1007/978-3-476-04311-5deos. To enhance the model’s ability to distinguish between successful and failed robot executions, we cluster failure video features to enable the model to identify patterns within.?For each cluster, we integrate a newly trained failure prompt into?the text encoder to represent the corresponding fa

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https://doi.org/10.1007/978-3-476-04311-5used in training and inference, mitigating performance degradation caused by sampling drift. Extensive experimental results demonstrate that DiffMatte not only reaches the state-of-the-art level on the mainstream Composition-1k test set, surpassing the previous best methods by . and . in the SAD met

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Beautiful Lies and Beautiful Truths, various domain adaptation methods in mitigating sensor-based domain differences. We also proposed?a . method to reduce domain disparities from?the perspectives of .ensity, .ntensity,?and .eometry, which effectively bridges the domain gap between different sensors. The experimental results on the CM

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Balzac’s Allegories of Energy in ,space. Next, the patch-wise visual features of the input image are selectively fused with the textual features of the salient visual concepts, leading to a mixed-up feature map with less defective content. Finally, a visual-semantic encoder is exploited to refine the derived feature map, which?is fu

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Balzac’s Allegories of Energy in ,ns and establishes connectivities in parallel, leveraging comprehensive visual and linguistic contexts. Experiments on CROHME 2014/2016/2019 and HME100K datasets demonstrate that NAMER not only outperforms the current state-of-the-art (SOTA) methods on ExpRate by 1.93%/2.35%/1.49%/0.62%, but also ac

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https://doi.org/10.1007/978-94-011-0898-0ce competitive with recent latent diffusion models. Finally, we obtain strong results outside of image generation when applying GIVT to panoptic segmentation and depth estimation with a VAE variant of the UViM framework.