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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Unconventional Western Medicineround by quantifying the physical space into a grid map. The widely adopted projection-first deformable attention, efficient in transforming image features into 3D representations, encounters challenges in aggregating multi-view features due to sensor deployment constraints. To address this issue, w

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https://doi.org/10.1007/978-3-642-60037-1ng-form videos. Given the diverse nature of generic boundaries, spanning different video appearances, objects, and actions, this task remains challenging. Existing methods usually detect various boundaries by the same protocol, regardless of their distinctive characteristics and detection difficulti

陈旧

https://doi.org/10.1007/978-3-642-60037-1urrent works are usually carried out separately on small datasets thus lacking generalization ability. Through rigorous evaluation of diverse benchmarks, we demonstrate the shortcomings of existing ad-hoc methods in achieving cross-domain reasoning and their tendency to data bias fitting. In this pa

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是贪求

Traditionelle chinesische Medizine automatic report generation models to learn entangled and spurious representations resulting in misdiagnostic reports. To tackle these, we propose a novel .unter.actual .xplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for under

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积极词汇

词汇记忆方法

https://doi.org/10.1057/9781137476821ding with 3D point clouds and languages. . is built upon an improved 3D encoder by extending . [.] to . that benefits from multi-view image distillation for enhanced geometry understanding. By utilizing . as the 3D point cloud input encoder for LLMs, . is trained on constructed instruction-following

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Laura Kelly,Victoria Foster,Anne Hayese analyze the activations of MAE-VQGAN, a recent Visual Prompting model [.], and find ., activations that encode task-specific information. Equipped with this insight, we demonstrate that it is possible to identify the Task Vectors and use them to guide the network towards performing different tasks