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80篇CVPR 2020论文分方向整理:目标检测/图像分割/姿态估计等

本文整理和分类80篇CVPR2020论文。

CVPR 2020在2月24日公布了所有接受论文ID,从论文ID公布以来,我们一直在对CVPR进行实时跟进,本文是对80篇CVPR 2020论文整理和分类,均有论文链接,部分含开源代码,涵盖的方向有:目标检测、目标跟踪、图像分割人脸识别姿态估计、三维点云、视频分析、模型加速、GAN、OCR等方向,文首文末有论文合集打包下载,分享给大家学习。

目标检测

1. Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

论文地址:

https://arxiv.org/abs/1912.02424   

代码:

https://github.com/sfzhang15/ATSS

2. Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector

论文地址:

https://arxiv.org/abs/1908.01998

图像分割

1. Semi-Supervised Semantic Image Segmentation with Self-correcting Networks

论文地址:

https://arxiv.org/abs/1811.07073

2. Deep Snake for Real-Time Instance Segmentation

论文地址:

https://arxiv.org/abs/2001.01629

3. CenterMask : Real-Time Anchor-Free Instance Segmentation

论文地址:

https://arxiv.org/abs/1911.06667

代码:

https://github.com/youngwanLEE/CenterMask

4. SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks

论文地址:

https://arxiv.org/abs/2003.00678

5. PolarMask: Single Shot Instance Segmentation with Polar Representation

论文地址:

https://arxiv.org/abs/1909.13226

代码:

https://github.com/xieenze/PolarMask

6. xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

论文地址:

https://arxiv.org/abs/1911.12676

7. BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

论文地址:

https://arxiv.org/abs/2001.00309

人脸识别

1. Towards Universal Representation Learning for Deep Face Recognition

论文地址:

https://arxiv.org/abs/2002.11841

2. Suppressing Uncertainties for Large-Scale Facial Expression Recognition       

论文地址:

https://arxiv.org/abs/2002.10392

代码:

https://github.com/kaiwang960112/Self-Cure-Network

3.Face X-ray for More General Face Forgery Detection

论文地址:

https://arxiv.org/pdf/1912.13458.pdf

目标跟踪

1.ROAM: Recurrently Optimizing Tracking Model

论文地址:

https://arxiv.org/abs/1907.12006

三维点云&重建

1. PF-Net: Point Fractal Network for 3D Point Cloud Completion

论文地址:

https://arxiv.org/abs/2003.00410

2. PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

论文地址:

https://arxiv.org/abs/2002.10876

代码:

https://github.com/liruihui/PointAugment/

3.Learning multiview 3D point cloud registration

论文地址:

https://arxiv.org/abs/2001.05119

4. C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds

论文地址:

https://arxiv.org/abs/1912.07009

5. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

论文地址:

https://arxiv.org/abs/1911.11236

6. Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image

论文地址:

https://arxiv.org/abs/2002.12212

7. Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

论文地址:

https://arxiv.org/abs/2003.01456

8. In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks

论文地址:

https://arxiv.org/pdf/1911.11924.pdf

图像处理

1. Learning to Shade Hand-drawn Sketches

论文地址:

https://arxiv.org/abs/2002.11812

2.Single Image Reflection Removal through Cascaded Refinement

论文地址:

https://arxiv.org/abs/1911.06634

3.Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data

论文地址:

https://arxiv.org/abs/2002.11297

4. Deep Image Harmonization via Domain Verification

论文地址:

https://arxiv.org/abs/1911.13239

代码:

https://github.com/bcmi/Image_Harmonization_Datasets

5. RoutedFusion: Learning Real-time Depth Map Fusion

论文地址:

https://arxiv.org/pdf/2001.04388.pdf

图像分类

1. Self-training with Noisy Student improves ImageNet classification

论文地址:

https://arxiv.org/abs/1911.04252

2. Image Matching across Wide Baselines: From Paper to Practice

论文地址:

https://arxiv.org/abs/2003.01587

3. Towards Robust Image Classification Using Sequential Attention Models

论文地址:

https://arxiv.org/abs/1912.02184

姿态估计

1. VIBE: Video Inference for Human Body Pose and Shape Estimation

论文地址:

https://arxiv.org/abs/1912.05656   

代码:

https://github.com/mkocabas/VIBE

2. Distribution-Aware Coordinate Representation for Human Pose Estimation

论文地址:

https://arxiv.org/abs/1910.06278   

代码:

https://github.com/ilovepose/DarkPose

3. 4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras

论文地址:

https://arxiv.org/abs/2002.12625

4. Optimal least-squares solution to the hand-eye calibration problem

论文地址:

https://arxiv.org/abs/2002.10838

5. D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

论文地址:

https://arxiv.org/abs/2003.01060

6. Multi-Modal Domain Adaptation for Fine-Grained Action Recognition

论文地址:

https://arxiv.org/abs/2001.09691

7. Distribution Aware Coordinate Representation for Human Pose Estimation

论文地址:

https://arxiv.org/abs/1910.06278

8. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation

论文地址:

https://arxiv.org/abs/1911.07524

9.PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation

论文地址:

https://arxiv.org/abs/1911.04231

视频分析

1. Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

论文地址:

https://arxiv.org/abs/2003.01455   

代码:

https://github.com/bbrattoli/ZeroShotVideoClassification

2. Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs

论文地址:

https://arxiv.org/abs/2003.00387

3. Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning

论文地址:

https://arxiv.org/abs/2003.00392

4. Object Relational Graph with Teacher-Recommended Learning for Video Captioning

论文地址:

https://arxiv.org/abs/2002.11566

5. Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

论文地址:

https://arxiv.org/abs/2002.11616

6. Blurry Video Frame Interpolation

论文地址:

https://arxiv.org/abs/2002.12259

7. Hierarchical Conditional Relation Networks for Video Question Answering

论文地址:

https://arxiv.org/abs/2002.10698   

8. Action Modifiers:Learning from Adverbs in Instructional Video

论文地址:

https://arxiv.org/abs/1912.06617     

OCR

1. ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

论文地址:

https://arxiv.org/abs/2002.10200

代码:

https://github.com/YuliangLiu/bezier_curve_text_spotting,https://github.com/aim-uofa/adet

GAN

1. Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models

论文地址:

https://arxiv.org/abs/1911.12287

代码:

https://github.com/giannisdaras/ylg

2. MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis

论文地址:

https://arxiv.org/abs/1903.06048

3. Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory

论文地址:

https://arxiv.org/abs/1911.04636

小样本&零样本

1. Improved Few-Shot Visual Classification

论文地址:

https://arxiv.org/pdf/1912.03432.pdf

2.Meta-Transfer Learning for Zero-Shot Super-Resolution

论文地址:

https://arxiv.org/abs/2002.12213

弱监督&无监督

1. Rethinking the Route Towards Weakly Supervised Object Localization

论文地址:

https://arxiv.org/abs/2002.11359

2. NestedVAE: Isolating Common Factors via Weak Supervision

论文地址:

https://arxiv.org/abs/2002.11576

3.Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation

论文地址:

https://arxiv.org/abs/1911.07450

4.Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

论文地址:

https://arxiv.org/abs/2003.01460

神经网络

1. Visual Commonsense R-CNN

论文地址:

https://arxiv.org/abs/2002.12204

2. GhostNet: More Features from Cheap Operations

论文地址:

https://arxiv.org/abs/1911.11907

代码:

https://github.com/iamhankai/ghostnet

3. Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral 

论文地址:

https://arxiv.org/abs/2003.01826

模型加速

1. GPU-Accelerated Mobile Multi-view Style Transfer

论文地址:

https://arxiv.org/abs/2003.00706

视觉常识

1. What it Thinks is Important is Important: Robustness Transfers through Input Gradients

论文地址:

https://arxiv.org/abs/1912.05699

2.Attentive Context Normalization for Robust Permutation-Equivariant Learning

论文地址:

https://arxiv.org/abs/1907.02545

3. Bundle Adjustment on a Graph Processor

论文地址:

https://arxiv.org/abs/2003.03134

代码:

https://github.com/joeaortiz/gbp

4. Transferring Dense Pose to Proximal Animal Classes

论文地址:

https://arxiv.org/abs/2003.00080

5. Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs

论文地址:

https://arxiv.org/abs/2003.00287

6. Learning in the Frequency Domain

论文地址:

https://arxiv.org/abs/2002.12416

7.Filter Grafting for Deep Neural Networks

论文地址:

https://arxiv.org/pdf/2001.05868.pdf

8.ClusterFit: Improving Generalization of Visual Representations

论文地址:

https://arxiv.org/abs/1912.03330

9.Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

论文地址:

https://arxiv.org/abs/2002.11927

10.Auto-Encoding Twin-Bottleneck Hashing

论文地址:

https://arxiv.org/abs/2002.11930

11.Learning Representations by Predicting Bags of Visual Words

论文地址:

https://arxiv.org/abs/2002.12247

12.Holistically-Attracted Wireframe Parsing

论文地址:

https://arxiv.org/abs/2003.01663

13.A General and Adaptive Robust Loss Function

论文地址:

https://arxiv.org/abs/1701.03077

14.A Characteristic Function Approach to Deep Implicit Generative Modeling

论文地址:

https://arxiv.org/abs/1909.07425

15.AdderNet: Do We Really Need Multiplications in Deep Learning? 

论文地址:

https://arxiv.org/pdf/1912.13200 

16.12-in-1: Multi-Task Vision and Language Representation Learning

论文地址:

https://arxiv.org/abs/1912.02315

17.Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks


论文地址:

https://arxiv.org/abs/1912.09393

18.CARS: Contunuous Evolution for Efficient Neural Architecture Search

论文地址:

https://arxiv.org/pdf/1909.04977.pdf

代码:

https://github.com/huawei-noah/CARS

19.Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training

论文地址:

https://arxiv.org/abs/2002.10638

代码:

https://github.com/weituo12321/PREVALENT

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理论姿态估计图像分割目标检测CVPR 2020
相关数据
图像分割技术

图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程。它是由图像处理到图像分析的关键步骤。现有的图像分割方法主要分以下几类:基于阈值的分割方法、基于区域的分割方法、基于边缘的分割方法以及基于特定理论的分割方法等。从数学角度来看,图像分割是将数字图像划分成互不相交的区域的过程。图像分割的过程也是一个标记过程,即把属于同一区域的像索赋予相同的编号。

人脸识别技术

广义的人脸识别实际包括构建人脸识别系统的一系列相关技术,包括人脸图像采集、人脸定位、人脸识别预处理、身份确认以及身份查找等;而狭义的人脸识别特指通过人脸进行身份确认或者身份查找的技术或系统。 人脸识别是一项热门的计算机技术研究领域,它属于生物特征识别技术,是对生物体(一般特指人)本身的生物特征来区分生物体个体。

神经网络技术

(人工)神经网络是一种起源于 20 世纪 50 年代的监督式机器学习模型,那时候研究者构想了「感知器(perceptron)」的想法。这一领域的研究者通常被称为「联结主义者(Connectionist)」,因为这种模型模拟了人脑的功能。神经网络模型通常是通过反向传播算法应用梯度下降训练的。目前神经网络有两大主要类型,它们都是前馈神经网络:卷积神经网络(CNN)和循环神经网络(RNN),其中 RNN 又包含长短期记忆(LSTM)、门控循环单元(GRU)等等。深度学习是一种主要应用于神经网络帮助其取得更好结果的技术。尽管神经网络主要用于监督学习,但也有一些为无监督学习设计的变体,比如自动编码器和生成对抗网络(GAN)。

图像处理技术

图像处理是指对图像进行分析、加工和处理,使其满足视觉、心理或其他要求的技术。 图像处理是信号处理在图像领域上的一个应用。 目前大多数的图像均是以数字形式存储,因而图像处理很多情况下指数字图像处理。

图像分类技术

图像分类,根据各自在图像信息中所反映的不同特征,把不同类别的目标区分开来的图像处理方法。它利用计算机对图像进行定量分析,把图像或图像中的每个像元或区域划归为若干个类别中的某一种,以代替人的视觉判读。

目标检测技术

一般目标检测(generic object detection)的目标是根据大量预定义的类别在自然图像中确定目标实例的位置,这是计算机视觉领域最基本和最有挑战性的问题之一。近些年兴起的深度学习技术是一种可从数据中直接学习特征表示的强大方法,并已经为一般目标检测领域带来了显著的突破性进展。

姿态估计技术

姿势估计是指检测图像和视频中的人物形象的计算机视觉技术,以便确定某人的某个肢体出现在图像中的位置。

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