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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: introspective
21#
發(fā)表于 2025-3-25 04:03:05 | 只看該作者
22#
發(fā)表于 2025-3-25 08:40:12 | 只看該作者
23#
發(fā)表于 2025-3-25 14:33:10 | 只看該作者
24#
發(fā)表于 2025-3-25 19:46:21 | 只看該作者
,Learning Chain of?Counterfactual Thought for?Bias-Robust Vision-Language Reasoning, to counterfactual scenarios. This enables LVLMs to explicitly reason step-by-step rather than relying on biased knowledge, leading to more generalizable solutions. Our extensive evaluation demonstrates that CoCT outperforms existing approaches on tasks requiring reasoning under knowledge bias. Our
25#
發(fā)表于 2025-3-25 21:26:49 | 只看該作者
Alte und kranke Frauen: 12 Biographienumber-free motion synthesis. Besides, based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion. Extensive experiments demonstrate the superior performance of our method and our capability to infer single and multi-human motions simultaneously.
26#
發(fā)表于 2025-3-26 01:53:22 | 只看該作者
27#
發(fā)表于 2025-3-26 08:20:48 | 只看該作者
28#
發(fā)表于 2025-3-26 09:40:39 | 只看該作者
,PISR: Polarimetric Neural Implicit Surface Reconstruction for?Textureless and?Specular Objects,-grid-based neural signed distance function to accelerate the reconstruction. Experimental results demonstrate that PISR achieves higher accuracy and robustness, with an L1 Chamfer distance of 0.5 mm and an F-score of 99.5% at 1?mm, while converging . faster than previous polarimetric surface reconstruction methods.
29#
發(fā)表于 2025-3-26 16:01:17 | 只看該作者
0302-9743 n; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-73241-6978-3-031-73242-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
30#
發(fā)表于 2025-3-26 20:31:32 | 只看該作者
Theorie und Empirie Lebenslangen Lernense . and . space DiffiT models and show SOTA performance on a variety of class-conditional and unconditional synthesis tasks at different resolutions. The Latent DiffiT model achieves a new SOTA FID score of . on . dataset while having ., . less parameters than other Transformer-based diffusion models such as MDT and DiT, respectively.
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