Measured 发表于 2025-3-26 23:17:11
http://reply.papertrans.cn/24/2342/234197/234197_31.pngInnocence 发表于 2025-3-27 04:16:43
http://reply.papertrans.cn/24/2342/234197/234197_32.png同谋 发表于 2025-3-27 09:10:15
http://reply.papertrans.cn/24/2342/234197/234197_33.png权宜之计 发表于 2025-3-27 11:48:45
0302-9743 missions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions..978-3-030-01239-7978-3-030-01240-3Series ISSN 0302-9743 Series E-ISSN 1611-3349注意力集中 发表于 2025-3-27 15:59:52
http://reply.papertrans.cn/24/2342/234197/234197_35.png艺术 发表于 2025-3-27 19:40:56
The Trade and Cooperation Agreement,rly exploit the information from the previous stage, an adaptive fusion block is devised to learn a dynamic integration of the current stage’s output and the previous stage’s output. Experiments on multiple datasets demonstrate that our proposed approach can improve the translation quality compared with previous single-stage unsupervised methods.跳脱衣舞的人 发表于 2025-3-28 00:21:19
The EU, ASEAN and Interregionalism clip for each sentence in the query with the help of a focusing guide. These levels are complementary – the top-level matching narrows the search while the part-level localization refines the results. On both ActivityNet Captions and modified LSMDC datasets, the proposed framework achieves remarkable performance gains (Project Page: .).惰性女人 发表于 2025-3-28 05:30:33
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraintsidelines towards practical integration of learned descriptors in Structure-from-Motion (SfM) pipelines, showing the good trade-off that GeoDesc delivers to 3D reconstruction tasks between accuracy and efficiency.Myosin 发表于 2025-3-28 06:40:11
http://reply.papertrans.cn/24/2342/234197/234197_39.pnginstill 发表于 2025-3-28 13:56:45
Find and Focus: Retrieve and Localize Video Events with Natural Language Queries clip for each sentence in the query with the help of a focusing guide. These levels are complementary – the top-level matching narrows the search while the part-level localization refines the results. On both ActivityNet Captions and modified LSMDC datasets, the proposed framework achieves remarkable performance gains (Project Page: .).