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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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樓主: Intimidate
51#
發(fā)表于 2025-3-30 11:42:55 | 只看該作者
,SpaRP: Fast 3D Object Reconstruction and?Pose Estimation from?Sparse Views,, they often lack sufficient controllability and tend to produce hallucinated regions that may not align with users’ expectations. In this paper, we explore an important scenario in which the input consists of one or a few unposed 2D images of a single object, with little or no overlap. We propose a
52#
發(fā)表于 2025-3-30 13:15:18 | 只看該作者
MMEarth: Exploring Multi-modal Pretext Tasks for Geospatial Representation Learning, unique opportunity to pair data from different modalities and sensors automatically based on geographic location and time, at virtually no human labor cost. We seize this opportunity to create ., a diverse multi-modal pretraining dataset at global scale. Using this new corpus of 1.2 million locatio
53#
發(fā)表于 2025-3-30 18:45:49 | 只看該作者
,Evolving Interpretable Visual Classifiers with?Large Language Models,owever, vision-language models, which compute similarity scores between images and class labels, are largely black-box, with limited interpretability, risk for bias, and inability to discover new visual concepts not written down. Moreover, in practical settings, the vocabulary for class names and at
54#
發(fā)表于 2025-3-31 00:25:20 | 只看該作者
55#
發(fā)表于 2025-3-31 01:52:31 | 只看該作者
56#
發(fā)表于 2025-3-31 07:42:28 | 只看該作者
,Ferret-UI: Grounded Mobile UI Understanding with?Multimodal LLMs,y to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced understanding of mobile UI screens, equipped with ., ., and . capabilities. Given that UI screens typically exhibit a more elongated aspect ratio and conta
57#
發(fā)表于 2025-3-31 10:49:39 | 只看該作者
,Bridging the?Pathology Domain Gap: Efficiently Adapting CLIP for?Pathology Image Analysis with?Limiization across diverse vision tasks. However, its effectiveness in pathology image analysis, particularly with limited labeled data, remains an ongoing area of investigation due to challenges associated with significant domain shifts and catastrophic forgetting. Thus, it is imperative to devise effi
58#
發(fā)表于 2025-3-31 17:22:46 | 只看該作者
,AugUndo: Scaling Up Augmentations for?Monocular Depth Completion and?Estimation,tion, and occlusions are amongst the many undesirable by-products of common data augmentation schemes that affect image reconstruction quality, and thus the training signal. Hence, typical augmentations on images viewed as essential to training pipelines in other vision tasks have seen limited use b
59#
發(fā)表于 2025-3-31 20:38:36 | 只看該作者
60#
發(fā)表于 2025-3-31 23:46:35 | 只看該作者
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