过分自信
发表于 2025-3-30 12:00:48
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不满分子
发表于 2025-3-30 13:03:00
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Exterior
发表于 2025-3-30 18:27:19
iMatching: Imperative Correspondence Learning,try and 3D reconstruction. Despite recent progress in data-driven models, feature correspondence learning is still limited by the lack of accurate per-pixel correspondence labels. To overcome this difficulty, we introduce a new self-supervised scheme, imperative learning (IL), for training feature c
或者发神韵
发表于 2025-3-30 21:12:21
,COSMU: Complete 3D Human Shape from Monocular Unconstrained Images,tive of this work is to reproduce high-quality details in regions of the reconstructed human body that are not visible in the input target. The proposed methodology addresses the limitations of existing approaches for reconstructing 3D human shapes from a single image, which cannot reproduce shape d
节省
发表于 2025-3-31 04:32:25
MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps,oal-oriented navigation or object interaction and manipulation). Commonly, 3D semantic reconstruction systems capture the entire scene in the same level of detail. However, certain tasks (.., object interaction) require a fine-grained and high-resolution map, particularly if the objects to interact
可憎
发表于 2025-3-31 07:33:34
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flamboyant
发表于 2025-3-31 09:16:55
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玛瑙
发表于 2025-3-31 16:20:52
Open Vocabulary Multi-label Video Classification,ect detection and image segmentation. Some recent works have focused on extending VLMs to open vocabulary . action classification in videos. However, previous methods fall short in holistic video understanding which requires the ability to . e.g., . in the video in an open vocabulary setting. We for
etiquette
发表于 2025-3-31 20:12:23
,Optimal Transport of Diverse Unsupervised Tasks for Robust Learning from Noisy Few-Shot Data,ansing offers a viable solution to address noisy labels in the general learning settings, it exacerbates information loss in FSL due to limited training data, resulting in inadequate model training. To best recover the underlying task manifold corrupted by the noisy labels, we resort to learning fro