Awesome-CVPR-Paper/CVPR2020 at master · yolanda3212/Awesome-CVPR-Paper

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公众号【计算机视觉联盟】后台回复 CVPR2020 下载最新论文

往年的请回复 CVPR2019

CVPR 2020

1.GhostNet: More Features from Cheap Operations（超越Mobilenet v3的架构）论文链接：https://arxiv.org/pdf/1911.11907arxiv.org 模型（在ARM CPU上的表现惊人）：https://github.com/iamhankai/ghostnetgithub.com

We beat other SOTA lightweight CNNs such as MobileNetV3 and FBNet.

AdderNet: Do We Really Need Multiplications in Deep Learning? （加法神经网络）在大规模神经网络和数据集上取得了非常好的表现论文链接：https://arxiv.org/pdf/1912.13200arxiv.org
Frequency Domain Compact 3D Convolutional Neural Networks （3dCNN压缩）论文链接：https://arxiv.org/pdf/1909.04977arxiv.org 开源代码：https://github.com/huawei-noah/CARSgithub.com
A Semi-Supervised Assessor of Neural Architectures （神经网络精度预测器 NAS）
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection （NAS 检测） backbone-neck-head一起搜索，三位一体
CARS: Contunuous Evolution for Efficient Neural Architecture Search (连续进化的NAS) 高效，具备可微和进化的多重优势，且能输出帕累托前研
On Positive-Unlabeled Classification in GAN （PU+GAN）
Learning multiview 3D point cloud registration（3D点云）论文链接：arxiv.org/abs/2001.05119
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition（细粒度动作识别）论文链接：arxiv.org/abs/2001.09691
Action Modifiers:Learning from Adverbs in Instructional Video 论文链接：arxiv.org/abs/1912.06617
PolarMask: Single Shot Instance Segmentation with Polar Representation（实例分割建模）论文链接：arxiv.org/abs/1909.13226 论文解读：https://zhuanlan.zhihu.com/p/84890413 开源代码：https://github.com/xieenze/PolarMask
Rethinking Performance Estimation in Neural Architecture Search（NAS）由于block wise neural architecture search中真正消耗时间的是performance estimation部分，本文针对 block wise的NAS找到了最优参数，速度更快，且相关度更高。
Distribution Aware Coordinate Representation for Human Pose Estimation（人体姿态估计）论文链接：arxiv.org/abs/1910.06278 Github：https://github.com/ilovepose/DarkPose 作者团队主页：https://ilovepose.github.io/coco/