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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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21#
發(fā)表于 2025-3-25 04:44:35 | 只看該作者
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發(fā)表于 2025-3-25 10:11:04 | 只看該作者
23#
發(fā)表于 2025-3-25 12:46:46 | 只看該作者
,nuCraft: Crafting High Resolution 3D Semantic Occupancy for?Unified 3D Scene Understanding,-Occ, a novel method that encodes occupancy data into a compact latent feature space using a VQ-VAE. This approach simplifies semantic occupancy prediction into feature simulation in the VQ latent space, making it easier and more memory-efficient. Our method enables direct generation of semantic occ
24#
發(fā)表于 2025-3-25 18:23:26 | 只看該作者
25#
發(fā)表于 2025-3-25 22:48:48 | 只看該作者
,PiTe: Pixel-Temporal Alignment for?Large Video-Language Model,multi-modal pre-training dataset PiTe-143k, the dataset provision of moving trajectories in pixel level for all individual objects, that appear and mention in the video and caption both, by our automatic annotation pipeline. Meanwhile, . demonstrates astounding capabilities on myriad video-related m
26#
發(fā)表于 2025-3-26 01:01:26 | 只看該作者
27#
發(fā)表于 2025-3-26 05:49:20 | 只看該作者
,FreeDiff: Progressive Frequency Truncation for?Image Editing with?Diffusion Models,ency signals for editing. Leveraging this insight, we introduce a novel fine-tuning free approach that employs progressive .qu.ncy truncation to refine the guidance of .usion models for universal editing tasks (.). Our method achieves comparable results with state-of-the-art methods across a variety
28#
發(fā)表于 2025-3-26 09:46:02 | 只看該作者
29#
發(fā)表于 2025-3-26 13:54:07 | 只看該作者
30#
發(fā)表于 2025-3-26 18:53:51 | 只看該作者
Text-Guided Video Masked Autoencoder,tion, we next introduce a unified framework for joint MAE and masked video-text contrastive learning. We show that across existing masking algorithms, unifying MAE and masked video-text contrastive learning improves downstream performance compared to pure MAE on a variety of video recognition tasks,
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