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Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati

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31#
發(fā)表于 2025-3-26 23:20:40 | 只看該作者
32#
發(fā)表于 2025-3-27 03:30:42 | 只看該作者
The Dream of a Democratic Cultureuncertainty based representation and incorporate it into a geometric matching framework. We show that our approach is able to rule out a considerable portion of false candidate regions even in a database composed of geographic areas with similar visual appearances.
33#
發(fā)表于 2025-3-27 09:19:14 | 只看該作者
34#
發(fā)表于 2025-3-27 13:15:10 | 只看該作者
0302-9743 on, ECCV 2014, held in Zurich, Switzerland, in September 2014.The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and
35#
發(fā)表于 2025-3-27 17:41:28 | 只看該作者
Men in the Household, Guild, and City, significant improvements in performance measured both in terms of Cumulative Match Characteristic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets.
36#
發(fā)表于 2025-3-27 18:59:03 | 只看該作者
,More On Demeter: Marsha Norman’s ‘,,ional bias. We propose to use multiple kernel learning (MKL) to achieve a robust integration of various feature maps. Experimental results show that the proposed model outperforms its counterparts in predicting webpage saliency.
37#
發(fā)表于 2025-3-27 22:58:31 | 只看該作者
38#
發(fā)表于 2025-3-28 03:46:16 | 只看該作者
The Dream of a Democratic Culturefaces, we formulate the constraint based on a locally linear embedding framework to promote local and global consistency on the shading layer. We demonstrate that combining the novel texture-aware image model and the novel surface normal based constraint can produce superior results to existing approaches.
39#
發(fā)表于 2025-3-28 06:39:56 | 只看該作者
Person Re-Identification Using Kernel-Based Metric Learning Methods significant improvements in performance measured both in terms of Cumulative Match Characteristic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets.
40#
發(fā)表于 2025-3-28 10:41:03 | 只看該作者
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