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Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

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21#
發(fā)表于 2025-3-25 05:14:56 | 只看該作者
22#
發(fā)表于 2025-3-25 09:08:01 | 只看該作者
Breivik in a Comparative Perspective,be performed in different spaces by the simple nearest neighbor approach using the learned class prototypes. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.
23#
發(fā)表于 2025-3-25 12:05:41 | 只看該作者
Michael A. Landesmann,Roberto Scazzierimay be reconstructed progressively. Extensive experiments on NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method achieves state-of-the-art results for monocular depth estimation and semantic segmentation.
24#
發(fā)表于 2025-3-25 16:57:38 | 只看該作者
25#
發(fā)表于 2025-3-25 20:31:27 | 只看該作者
Bayesian Semantic Instance Segmentation in Open Set Worldsimulated annealing optimization equipped with an efficient image partition sampler. We show empirically that our method is competitive with state-of-the-art supervised methods on known classes, but also performs well on unknown classes when compared with unsupervised methods.
26#
發(fā)表于 2025-3-26 03:28:02 | 只看該作者
27#
發(fā)表于 2025-3-26 05:17:23 | 只看該作者
stagNet: An Attentive Semantic RNN for Group Activity Recognitiones and capturing inter-group relationships. Moreover, we adopt a spatio-temporal attention model to attend to key persons/frames for improved performance. Two widely-used datasets are employed for performance evaluation, and the extensive results demonstrate the superiority of our method.
28#
發(fā)表于 2025-3-26 10:15:10 | 只看該作者
29#
發(fā)表于 2025-3-26 16:10:20 | 只看該作者
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimationmay be reconstructed progressively. Extensive experiments on NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method achieves state-of-the-art results for monocular depth estimation and semantic segmentation.
30#
發(fā)表于 2025-3-26 19:32:24 | 只看該作者
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