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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: onychomycosis
51#
發(fā)表于 2025-3-30 09:49:33 | 只看該作者
,Learning Camouflaged Object Detection from?Noisy Pseudo Label,-intensive. Although weakly supervised methods offer higher annotation efficiency, their performance is far behind due to the unclear visual demarcations between foreground and background in camouflaged images. In this paper, we explore the potential of using boxes as prompts in camouflaged scenes a
52#
發(fā)表于 2025-3-30 14:38:23 | 只看該作者
,Weakly Supervised 3D Object Detection via?Multi-level Visual Guidance, few accurate 3D annotations, we propose a framework to study how to leverage constraints between 2D and 3D domains without requiring any 3D labels. Specifically, we employ visual data from three perspectives to establish connections between 2D and 3D domains. First, we design a feature-level constr
53#
發(fā)表于 2025-3-30 20:00:18 | 只看該作者
,Deblur ,-NeRF: NeRF from?Motion-Blurred Events under High-speed or?Low-light Conditions,s underperform. However, event cameras also suffer from motion blur, especially under these challenging conditions, contrary to what most think. This is due to the limited bandwidth of the event sensor pixel, which is mostly proportional to the light intensity. Thus, to ensure event cameras can trul
54#
發(fā)表于 2025-3-31 00:10:55 | 只看該作者
55#
發(fā)表于 2025-3-31 02:08:36 | 只看該作者
56#
發(fā)表于 2025-3-31 05:23:09 | 只看該作者
57#
發(fā)表于 2025-3-31 09:16:26 | 只看該作者
,Motion Mamba: Efficient and?Long Sequence Motion Generation,emains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a significant direction upon building motion generation model. Nevertheless, adapting SSMs t
58#
發(fā)表于 2025-3-31 17:22:10 | 只看該作者
59#
發(fā)表于 2025-3-31 19:27:18 | 只看該作者
60#
發(fā)表于 2025-4-1 01:15:13 | 只看該作者
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