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

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Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding,multi-view information respectively. We also propose new stereo based rainy datasets for benchmarking. Experiments on both monocular and the newly proposed stereo rainy datasets demonstrate that the proposed method achieves the state-of-the-art performance.

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DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks,s the daunting task of aggressively quantizing lightweight networks such as MobileNetV1, MobileNetV2, and ShuffleNetV2. DBQ achieves state-of-the art results with minimal training overhead and provides the best (pareto-optimal) accuracy-complexity trade-off.

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All at Once: Temporally Adaptive Multi-frame Interpolation with Advanced Motion Modeling,uracy when complex motion segments are encountered. Results on the Adobe240 dataset show that the proposed method generates visually pleasing, temporally consistent frames, outperforms the current best off-the-shelf method by 1.57 dB in PSNR with 8 times smaller model and 7.7 times faster. The propo

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A Broader Study of Cross-Domain Few-Shot Learning, as crop disease images, but additionally those that present with an increasing dissimilarity to natural images, such as satellite images, dermatology images, and radiology images. Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transf

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SemifreddoNets: Partially Frozen Neural Networks for Efficient Computer Vision Systems,antages of a fully configurable model for many use cases. Furthermore, our system uses repeatable blocks, therefore it has the flexibility to adjust model complexity without requiring any hardware change. The hardware implementation of SemifreddoNets provides up to an order of magnitude reduction in

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