CODA 发表于 2025-3-23 13:46:51

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Palter 发表于 2025-3-23 14:25:48

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制定法律 发表于 2025-3-23 19:36:36

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直觉好 发表于 2025-3-23 22:23:39

,Content-Aware Radiance Fields: Aligning Model Complexity with Scene Intricacy Through Learned Bitwint 3D content by that training models for each individual scene. This unique characteristic of scene representation and per-scene training distinguishes radiance field models from other neural models, because complex scenes necessitate models with higher representational capacity and vice versa. In

Dignant 发表于 2025-3-24 02:32:57

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遵循的规范 发表于 2025-3-24 08:39:29

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Complement 发表于 2025-3-24 12:53:19

Event Camera Data Dense Pre-training,mera data. Our approach utilizes solely event data for training..Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not

essential-fats 发表于 2025-3-24 16:48:13

,Distractors-Immune Representation Learning with Cross-Modal Contrastive Regularization for Change Cd viewpoint changes). Under these distractors, unchanged objects often appear pseudo changes about location and scale, and certain objects might overlap others, resulting in perturbational and discrimination-degraded features between two images. However, most existing methods directly capture the di

Ascribe 发表于 2025-3-24 21:54:08

Rethinking Image-to-Video Adaptation: An Object-Centric Perspective,mage-to-video adaptation paradigms use lightweight adapters for temporal modeling on top of the spatial module. However, these attempts are subject to limitations in efficiency and interpretability. In this paper, we propose a novel and efficient image-to-video adaptation strategy from the object-ce

sparse 发表于 2025-3-25 02:51:14

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