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

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,MARs: Multi-view Attention Regularizations for Patch-Based Feature Recognition of Space Terrain,ocus. We thoroughly analyze many modern metric learning losses with and without MARs and demonstrate improved terrain-feature recognition performance by upwards of 85%. We additionally introduce the Luna-1 dataset, consisting of Moon crater landmarks and reference navigation frames from NASA mission

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,Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs,s are formatted for instruction-following with region annotations to facilitate precise referring and grounding. To augment the model’s reasoning ability, we further compile a dataset for advanced tasks, including detailed description, conversations, and function inference. After training on the cur

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,Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limi (DVC) techniques to mitigate overfitting issues. Finally, we present the Doublet Multimodal Contrastive Loss (DMCL) for fine-tuning CLIP for pathology tasks. We demonstrate that Path-CLIP adeptly adapts pre-trained CLIP to downstream pathology tasks, yielding competitive results. Specifically, Path

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,AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation,g, geometric transformations to the coordinates of the output depth, warping the depth map back to the original reference frame. This enables computing the reconstruction losses using the original images and sparse depth maps, eliminating the pitfalls of naive loss computation on the augmented input

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,Minimalist Vision with Freeform Pixels, major advantages. First, it naturally tends to preserve the privacy of individuals in the scene since the captured information is inadequate for extracting visual details. Second, since the number of measurements made by a minimalist camera is very small, we show that it can be fully self-powered,

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