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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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樓主: Magnanimous
31#
發(fā)表于 2025-3-26 23:32:16 | 只看該作者
32#
發(fā)表于 2025-3-27 03:14:49 | 只看該作者
https://doi.org/10.1007/978-1-349-24924-4ove the reconstruction quality. The stochastic tomography is based on Monte-Carlo (MC) radiative transfer. It is formulated and implemented in a coarse-to-fine form, making it scalable to large fields.
33#
發(fā)表于 2025-3-27 05:26:29 | 只看該作者
https://doi.org/10.1007/978-1-349-24924-4h does not necessarily align with visual coherency. Our method ensures that not only are paired images and texts close, but the expected image-image and text-text relationships are also observed. Our approach improves the results of cross-modal retrieval on four datasets compared to five baselines.
34#
發(fā)表于 2025-3-27 10:20:56 | 只看該作者
35#
發(fā)表于 2025-3-27 16:06:44 | 只看該作者
Joint Optimization for Multi-person Shape Models from Markerless 3D-Scans, sufficient to achieve competitive performance on the challenging FAUST surface correspondence benchmark. The training and evaluation code will be made available for research purposes to facilitate end-to-end shape model training on novel datasets with minimal setup cost.
36#
發(fā)表于 2025-3-27 21:24:32 | 只看該作者
Hidden Footprints: Learning Contextual Walkability from 3D Human Trails,a contextual adversarial loss. Using this strategy, we demonstrate a model that learns to predict a walkability map from a single image. We evaluate our model on the Waymo and Cityscapes datasets, demonstrating superior performance compared to baselines and state-of-the-art models.
37#
發(fā)表于 2025-3-28 01:27:11 | 只看該作者
Self-supervised Learning of Audio-Visual Objects from Video,applying it to non-human speakers, including cartoons and puppets. Our model significantly outperforms other self-supervised approaches, and obtains performance competitive with methods that use supervised face detection.
38#
發(fā)表于 2025-3-28 04:40:39 | 只看該作者
39#
發(fā)表于 2025-3-28 07:50:09 | 只看該作者
Preserving Semantic Neighborhoods for Robust Cross-Modal Retrieval,h does not necessarily align with visual coherency. Our method ensures that not only are paired images and texts close, but the expected image-image and text-text relationships are also observed. Our approach improves the results of cross-modal retrieval on four datasets compared to five baselines.
40#
發(fā)表于 2025-3-28 12:16:05 | 只看該作者
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