Titlebook: Computer Vision – ECCV 2020 Workshops; Glasgow, UK, August Adrien Bartoli,Andrea Fusiello Conference proceedings 2020 Springer Nature Swit

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書目名稱Computer Vision – ECCV 2020 Workshops網(wǎng)絡公開度學科排名




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書目名稱Computer Vision – ECCV 2020 Workshops被引頻次學科排名




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書目名稱Computer Vision – ECCV 2020 Workshops年度引用學科排名




書目名稱Computer Vision – ECCV 2020 Workshops讀者反饋




書目名稱Computer Vision – ECCV 2020 Workshops讀者反饋學科排名

招人嫉妒

SegBlocks: Towards Block-Based Adaptive Resolution Networks for Fast Segmentation Our method dynamically splits the image into blocks and processes low-complexity regions at a lower resolution. Our novel BlockPad module, implemented in CUDA, replaces zero-padding in order to prevent the discontinuities at patch borders of which existing methods suffer, while keeping memory consu

Paradox

Weight-Dependent Gates for Differentiable Neural Network PruningWe argue that the pruning decision should depend on the convolutional weights, in other words, it should be a learnable function of filter weights. We thus construct the weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary filter gates to prune or keep the

草率男

鴕鳥

An Efficient Method for Face Quality Assessment on the Edgefirst step generates multiple detections for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detections of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just app

本土

Efficient Approximation of Filters for High-Accuracy Binary Convolutional Neural Networksfull-precision convolutional filter by sum of binary filters with multiplicative and additive scaling factors. We present closed form solutions to the proposed methods. We perform experiments on binary neural networks with binary activations and pre-trained neural networks with full-precision activa

本土

One Weight Bitwidth to Rule Them Allially important for applications where memory storage is limited. However, when aiming for quantization without accuracy degradation, different tasks may end up with different bitwidths. This creates complexity for software and hardware support and the complexity accumulates when one considers mixed

綠州

全國性

Post Training Mixed-Precision Quantization Based on Key Layers Selectionsimple to use, they have gained considerable attention. However, when the model is quantized below 8-bits, significant accuracy degradation will be involved. This paper seeks to address this problem by building mixed-precision inference networks based on key activation layers selection. In post trai

extinct

Subtensor Quantization for Mobilenets not all DNN designs are friendly to quantization. For example, the popular Mobilenet architecture has been tuned to reduce parameter size and computational latency with separable depthwise convolutions, but not all quantization algorithms work well and the accuracy can suffer against its float poin