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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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31#
發(fā)表于 2025-3-26 21:09:12 | 只看該作者
Semi-dense 3D Reconstruction with a Stereo Event Camerahas no special requirements on either the motion of the stereo event-camera rig or on prior knowledge about the scene. Experiments demonstrate our method can deal with both texture-rich scenes as well as sparse scenes, outperforming state-of-the-art stereo methods based on event data image representations.
32#
發(fā)表于 2025-3-27 02:16:49 | 只看該作者
Conference proceedings 2018, ECCV 2018, held in Munich, Germany, in September 2018..The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical?sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstructi
33#
發(fā)表于 2025-3-27 09:15:39 | 只看該作者
34#
發(fā)表于 2025-3-27 12:47:39 | 只看該作者
35#
發(fā)表于 2025-3-27 13:48:47 | 只看該作者
Diverse Image-to-Image Translation via Disentangled Representationsy reduces mode collapse. To handle unpaired training data, we introduce a novel cross-cycle consistency loss. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks. We validate the effectiveness of our approach through extensive evaluation.
36#
發(fā)表于 2025-3-27 21:00:15 | 只看該作者
37#
發(fā)表于 2025-3-28 00:00:09 | 只看該作者
Convolutional Networks with Adaptive Inference Graphsove directly to a layer that can distinguish fine-grained differences? Currently, a network would first need to execute sometimes hundreds of intermediate layers that specialize in unrelated aspects. Ideally, the more a network already knows about an image, the better it should be at deciding which
38#
發(fā)表于 2025-3-28 03:24:25 | 只看該作者
Progressive Neural Architecture Search based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures in order of increasing complexity, while simultaneously learning a surrogate model to guide the search through structure space. Dir
39#
發(fā)表于 2025-3-28 07:39:19 | 只看該作者
40#
發(fā)表于 2025-3-28 12:07:28 | 只看該作者
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