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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: Coolidge
11#
發(fā)表于 2025-3-23 11:25:33 | 只看該作者
,Generating Physically Realistic and?Directable Human Motions from?Multi-modal Inputs,ibits the key capabilities of . to out-of-sync input commands, . elements from multiple motion sequences, and . unspecified parts of motions from sparse multimodal input. We demonstrate these key capabilities for an MHC learned over a dataset of 87 diverse skills and showcase different multi-modal u
12#
發(fā)表于 2025-3-23 17:03:52 | 只看該作者
13#
發(fā)表于 2025-3-23 19:38:51 | 只看該作者
,PathMMU: A Massive Multimodal Expert-Level Benchmark for?Understanding and?Reasoning in?Pathology,evel performance benchmark for PathMMU. We conduct extensive evaluations, including zero-shot assessments of 14 open-sourced and 4 closed-sourced LMMs and their robustness to image corruption. We also fine-tune representative LMMs to assess their adaptability to PathMMU. The empirical findings indic
14#
發(fā)表于 2025-3-24 01:36:36 | 只看該作者
,RealGen: Retrieval Augmented Generation for?Controllable Traffic Scenarios,amples in a gradient-free way, which may originate from templates or tagged scenarios. This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios, compose various behaviors, and produce critical scenarios. Evaluations show that RealGen offers
15#
發(fā)表于 2025-3-24 03:55:58 | 只看該作者
,ADen: Adaptive Density Representations for?Sparse-View Camera Pose Estimation,re space of rotation uniformly?by brute-force. This leads to an inevitable trade-off between?high sample density, which improves model precision, and sample efficiency that determines the runtime. In this paper, we propose ADen to unify the two frameworks by employing a generator?and a discriminator
16#
發(fā)表于 2025-3-24 10:19:27 | 只看該作者
17#
發(fā)表于 2025-3-24 12:35:28 | 只看該作者
,ViLA: Efficient Video-Language Alignment for?Video Question Answering,he state-of-the-art methods on the video question-answering benchmarks: . on STAR Interaction, . on STAR average with . speed up, ours 2-frames out-perform SeViLA 4-frames on the VLEP dataset with . speed-up. Code will be available at
18#
發(fā)表于 2025-3-24 16:46:22 | 只看該作者
19#
發(fā)表于 2025-3-24 22:52:06 | 只看該作者
20#
發(fā)表于 2025-3-25 02:24:55 | 只看該作者
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