喜悦
发表于 2025-3-21 19:47:08
书目名称Computer Vision – ECCV 2022 Workshops影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0234284<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0234284<br><br> <br><br>
Dri727
发表于 2025-3-21 22:46:48
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Presbyopia
发表于 2025-3-22 02:54:11
Trans6D: Transformer-Based 6D Object Pose Estimation and Refinementdows, cross-attention, and token pooling operations, which is used to predict dense 2D-3D correspondence maps; (ii) a pure Transformer-based pose refinement module (Trans6D+) which refines the estimated poses iteratively. Extensive experiments show that the proposed approach achieves state-of-the-ar
overrule
发表于 2025-3-22 07:14:55
Learning to Estimate Multi-view Pose from Object Silhouettes cues for multi-view relationships in a data-driven way. We show that our network generalizes to unseen synthetic and real object instances under reasonable assumptions about the input pose distribution of the images, and that the estimates are suitable to initialize state-of-the-art 3D reconstructi
Obsessed
发表于 2025-3-22 08:44:25
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nautical
发表于 2025-3-22 16:57:44
Fuse and Attend: Generalized Embedding Learning for Art and Sketchesmains. During training, given a query image from a domain, we employ gated fusion and attention to generate a positive example, which carries a broad notion of the semantics of the query object category (from across multiple domains). By virtue of Contrastive Learning, we pull the embeddings of the
nautical
发表于 2025-3-22 17:03:17
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连系
发表于 2025-3-22 22:45:08
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endure
发表于 2025-3-23 02:07:53
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GLIB
发表于 2025-3-23 05:53:11
Lothar Lammersen,Robert Schwagers. To tackle these limitations, we propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets (left, right, top, bottom) that are homogeneous, so that it can tell which direction is uncer