Awesome-CVPR-Paper
更新2020年最新CVPR下载链接!
CVPR论文收集,包含但不限于2017、2018、2019、2020文章,会持续更新
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.
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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
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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
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A Semi-Supervised Assessor of Neural Architectures (神经网络精度预测器 NAS)
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Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection (NAS 检测) backbone-neck-head一起搜索, 三位一体
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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
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On Positive-Unlabeled Classification in GAN (PU+GAN)
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Learning multiview 3D point cloud registration(3D点云) 论文链接:arxiv.org/abs/2001.05119
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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
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Action Modifiers:Learning from Adverbs in Instructional Video 论文链接:arxiv.org/abs/1912.06617
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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
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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。
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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/
更新
- 视觉常识R-CNN,Visual Commonsense R-CNN
https://arxiv.org/abs/2002.12204
- Out-of-distribution图像检测
https://arxiv.org/abs/2002.11297
- 模糊视频帧插值,Blurry Video Frame Interpolation
https://arxiv.org/abs/2002.12259
- 元迁移学习零样本超分
https://arxiv.org/abs/2002.12213
- 3D室内场景理解
https://arxiv.org/abs/2002.12212
6.从有偏训练生成无偏场景图
https://arxiv.org/abs/2002.11949
- 自动编码双瓶颈哈希
https://arxiv.org/abs/2002.11930
- 一种用于人类轨迹预测的社会时空图卷积神经网络
https://arxiv.org/abs/2002.11927
- 面向面向深度人脸识别的通用表示学习
https://arxiv.org/abs/2002.11841
- 视觉表示泛化性
https://arxiv.org/abs/1912.03330
- 减弱上下文偏差
https://arxiv.org/abs/2002.11812
- 可迁移元技能的无监督强化学习
https://arxiv.org/abs/1911.07450
- 快速准确时空视频超分
https://arxiv.org/abs/2002.11616
- 对象关系图Teacher推荐学习的视频captioning
https://arxiv.org/abs/2002.11566
- 弱监督物体定位路由再思考
https://arxiv.org/abs/2002.11359
- 通过预培训学习视觉和语言导航的通用代理
https://arxiv.org/pdf/2002.10638.pdf
- GhostNet轻量级神经网络
https://arxiv.org/pdf/1911.11907.pdf
- AdderNet:在深度学习中,我们真的需要乘法吗?
https://arxiv.org/pdf/1912.13200.pdf
- CARS:高效神经结构搜索的持续进化
https://arxiv.org/abs/1909.04977
- 通过协作式的迭代级联微调来移除单图像中的反射
https://arxiv.org/abs/1911.06634
- 深度神经网络的滤波嫁接
https://arxiv.org/pdf/2001.05868.pdf
- PolarMask:将实例分割统一到FCN
https://arxiv.org/pdf/1909.13226.pdf
- 半监督语义图像分割
https://arxiv.org/pdf/1811.07073.pdf
- 通过选择性的特征再生来抵御通用攻击
https://arxiv.org/pdf/1906.03444.pdf
- 实时的基于细粒度草图的图像检索
https://arxiv.org/abs/2002.10310
- 用子问题询问VQA模型
https://arxiv.org/abs/1906.03444
- 从2D范例中学习神经三维纹理空间
https://geometry.cs.ucl.ac.uk/projects/2020/neuraltexture/
- NestedVAE:通过薄弱的监督来隔离共同因素
https://arxiv.org/abs/2002.11576
- 实现多未来轨迹预测
https://arxiv.org/pdf/1912.06445.pdf
- 使用序列注意力模型进行稳健的图像分类
https://arxiv.org/pdf/1912.02184
CVPR 2017
CVPR 2018
CVPR 2019
2018 CVPR近年来最佳论文Taskonomy: Disentangling Task Transfer LearningAmir R. Zamir, Stanford University; et al.
William Shen, Stanford University
Leonidas Guibas, Stanford University
Jitendra Malik, University of California Berkeley
Silvio Savarese, Stanford University
Laurens van der Maaten, Facebook AI Research
Kilian Q. Weinberger, Cornell University
Oncel Tuzel, Apple Inc.
Josh Susskind, Apple Inc.
Wenda Wang, Apple Inc.
Russ Webb, Apple Inc.
Shaoqing Ren, Microsoft Research
Jian Sun, Microsoft Research
Steven M. Seitz, University of Washington
Mark Segal, Google
Jonathon Shlens, Google
Sudheendra Vijayanarasimhan, Google
Jay Yagnik, Google
Mingyi He, Northwestern Polytechnical University
Mat Cook, Microsoft Research
Toby Sharp, Microsoft Research
Mark Finocchio, Microsoft Research
Richard Moore, Microsoft Research
Alex Kipman, Microsoft Research
Andrew Blake, Microsoft Research
Xiaoou Tang, The Chinese University of Hong Kong
Philip Torr, University of Oxford
Andrew Fitzgibbon, Microsoft Research
Thomas Hodmann, Google
Kurt COrnelis, Katholieke Universiteit Leuven
Luc Van Gool, ETH Zurich
Martial Hebert, Carnegie Mellon University
Pascal Fua, École Polytechnique Fédérale de Lausanne
Terry E. Boult, University of Colorado
Andrew Zisserman, University of Oxford
Peter Meer, Rutgers University