CVPR2021最新接收论文合集！22个方向100+篇论文汇总｜持续更新
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CVPR2021结果已出，本文为CVPR最新接收论文的资源汇总贴，附有相关文章与代码链接。 >>加入极市CV技术交流群，走在计算机视觉的最前沿
https://github.com/extreme-assistant/CVPR2021-Paper-Code-Interpretation/blob/master/CVPR2021.md
官网链接：http://cvpr2021.thecvf.com
时间：2021年6月19日-6月25日
论文接收公布时间：2021年2月28日
1.CVPR2021接受论文/代码分方向整理
分类目录：
1. 检测
图像目标检测(Image Object Detection)
视频目标检测(Video Object Detection)
三维目标检测(3D Object Detection)
动作检测(Activity Detection)
异常检测(Anomally Detetion)
2. 图像分割(Image Segmentation)
全景分割(Panoptic Segmentation)
语义分割(Semantic Segmentation)
实例分割(Instance Segmentation)
抠图(Matting)
3. 图像处理(Image Processing)
图像复原(Image Restoration)/超分辨率(Super Resolution)
图像阴影去除(Image Shadow Removal)
图像去噪/去模糊/去雨去雾(Image Denoising)
图像编辑(Image Edit)
图像翻译(Image Translation))
4. 人脸(Face)
5. 目标跟踪(Object Tracking)
6. 重识别(Re-Identification)
7. 医学影像(Medical Imaging)
8. GAN/生成式/对抗式(GAN/Generative/Adversarial)
9. 估计(Estimation)
人体姿态估计(Human Pose Estimation)
光流/位姿/运动估计(Flow/Pose/Motion Estimation)
深度估计(Depth Estimation)
10. 三维视觉(3D Vision)
三维点云(3D Point Cloud)
三维重建(3D Reconstruction)
11. 神经网络架构(Neural Network Structure)
Transformer
图神经网络(GNN)
12. 神经网络架构搜索(NAS)
13. 数据处理(Data Processing)
数据增广(Data Augmentation)
归一化/正则化(Batch Normalization)
图像聚类(Image Clustering)
14. 模型压缩(Model Compression)
知识蒸馏(Knowledge Distillation)
15. 模型评估(Model Evaluation)
16. 数据集(Database)
17. 主动学习(Active Learning)
18. 小样本学习/零样本(Few-shot Learning)
19. 持续学习(Continual Learning/Life-long Learning)
20. 视觉推理(Visual Reasoning)
21. 迁移学习/domain/自适应
22. 对比学习(Contrastive Learning)
暂无分类
检测
图像目标检测(Image Object Detection)
[7] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection(小样本目标检测的语义关系推理)
paper：https://arxiv.org/abs/2103.01903
[6] General Instance Distillation for Object Detection(通用实例蒸馏技术在目标检测中的应用)
paper：https://arxiv.org/abs/2103.02340
[5] Instance Localization for Self-supervised Detection Pretraining(自监督检测预训练的实例定位)
paper：https://arxiv.org/pdf/2102.08318.pdf
code：https://github.com/limbo0000/InstanceLoc
[4] Multiple Instance Active Learning for Object Detection（用于对象检测的多实例主动学习）
paper：https://github.com/yuantn/MIAL/raw/master/paper.pdf
code：https://github.com/yuantn/MIAL
[3] Towards Open World Object Detection(开放世界中的目标检测)
paper：Towards Open World Object Detection
code：https://github.com/JosephKJ/OWOD
[2] Positive-Unlabeled Data Purification in the Wild for Object Detection(野外检测对象的阳性无标签数据提纯)
[1] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
paper：https://arxiv.org/pdf/2011.09094.pdf
解读：
无监督预训练检测器：https://www.zhihu.com/question/432321109/answer/1606004872
视频目标检测(Video Object Detection)
[3] Depth from Camera Motion and Object Detection(相机运动和物体检测的深度)
paper：https://arxiv.org/abs/2103.01468
[2] There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge(多模态知识提取的自监督多目标检测与有声跟踪)
paper：https://arxiv.org/abs/2103.01353
project：http://rl.uni-freiburg.de/research/multimodal-distill
[1] Dogfight: Detecting Drones from Drone Videos（从无人机视频中检测无人机）
三维目标检测(3D object detection)
[2] 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection(利用IoU预测进行半监督3D对象检测)
paper：https://arxiv.org/pdf/2012.04355.pdf
code：https://github.com/THU17cyz/3DIoUMatch
project：https://thu17cyz.github.io/3DIoUMatch/
video：https://youtu.be/nuARjhkQN2U
[1] Categorical Depth Distribution Network for Monocular 3D Object Detection(用于单目三维目标检测的分类深度分布网络)
paper：https://arxiv.org/abs/2103.01100
动作检测(Activity Detection)
[1] Coarse-Fine Networks for Temporal Activity Detection in Videos
paper：https://arxiv.org/abs/2103.01302
异常检测(Anomally Detetion)
[1] Multiresolution Knowledge Distillation for Anomaly Detection(用于异常检测的多分辨率知识蒸馏)
paper：https://arxiv.org/abs/2011.11108
图像分割(Image Segmentation)
[2] Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
paper：https://arxiv.org/abs/2012.06166
code：https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation
[1] PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation(语义流经点以进行航空图像分割)
全景分割(Panoptic Segmentation)
[2] Cross-View Regularization for Domain Adaptive Panoptic Segmentation(用于域自适应全景分割的跨视图正则化)
paper：https://arxiv.org/abs/2103.02584
[1] 4D Panoptic LiDAR Segmentation（4D全景LiDAR分割）
paper：https://arxiv.org/abs/2102.12472
语义分割(Semantic Segmentation)
[2] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市规模3D点云的语义分割：数据集，基准和挑战)
paper：https://arxiv.org/abs/2009.03137
code：https://github.com/QingyongHu/SensatUrban
[1] PLOP: Learning without Forgetting for Continual Semantic Segmentation（PLOP：学习而不会忘记连续的语义分割）
paper：https://arxiv.org/abs/2011.11390
实例分割(Instance Segmentation)
[1] End-to-End Video Instance Segmentation with Transformers(使用Transformer的端到端视频实例分割)
paper：https://arxiv.org/abs/2011.14503
抠图(Matting)
[1] Real-Time High Resolution Background Matting
paper：https://arxiv.org/abs/2012.07810
code：https://github.com/PeterL1n/BackgroundMattingV2
project：https://grail.cs.washington.edu/projects/background-matting-v2/
video：https://youtu.be/oMfPTeYDF9g
9. 估计(Estimation)
人体姿态估计(Human Pose Estimation)
[2] CanonPose: Self-supervised Monocular 3D Human Pose Estimation in the Wild（野外自监督的单眼3D人类姿态估计）
[1] PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers（具有透视作物层的3D姿势的几何感知神经重建）
paper：https://arxiv.org/abs/2011.13607
光流/位姿/运动估计(Flow/Pose/Motion Estimation)
[3] GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation(用于单眼6D对象姿态估计的几何引导直接回归网络)
paper：http://arxiv.org/abs/2102.12145
code：https://github.com/THU-DA-6D-Pose-Group/GDR-Net
[2] Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments(在动态室内环境中，通过空间划分的鲁棒神经路由可实现摄像机的重新定位)
paper：https://arxiv.org/abs/2012.04746
project：https://ai.stanford.edu/~hewang/
[1] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通过3D扫描同步进行多主体分割和运动估计)
paper：https://arxiv.org/pdf/2101.06605.pdf
code：https://github.com/huangjh-pub/multibody-sync
深度估计(Depth Estimation)
图像处理(Image Processing)
图像复原(Image Restoration)/超分辨率(Super Resolution)
[3] Multi-Stage Progressive Image Restoration(多阶段渐进式图像复原)
paper：https://arxiv.org/abs/2102.02808
code：https://github.com/swz30/MPRNet
[2] Data-Free Knowledge Distillation For Image Super-Resolution(DAFL算法的SR版本)
[1] AdderSR: Towards Energy Efficient Image Super-Resolution(将加法网路应用到图像超分辨率中)
paper：https://arxiv.org/pdf/2009.08891.pdf
code：https://github.com/huawei-noah/AdderNet
解读：华为开源加法神经网络
图像阴影去除(Image Shadow Removal)
[1] Auto-Exposure Fusion for Single-Image Shadow Removal(用于单幅图像阴影去除的自动曝光融合)
paper：https://arxiv.org/abs/2103.01255
code：https://github.com/tsingqguo/exposure-fusion-shadow-removal
图像去噪/去模糊/去雨去雾(Image Denoising)
[1] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects(快速移动物体的去模糊和形状恢复)
paper：https://arxiv.org/abs/2012.00595
code：https://github.com/rozumden/DeFMO
video：https://www.youtube.com/watch?v=pmAynZvaaQ4
图像编辑(Image Edit)
[1] Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing（利用GAN中潜在的空间维度进行实时图像编辑）
图像翻译（Image Translation）
[2] Image-to-image Translation via Hierarchical Style Disentanglement
paper：https://arxiv.org/abs/2103.01456
code：https://github.com/imlixinyang/HiSD
[1] Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation(样式编码：用于图像到图像翻译的StyleGAN编码器)
paper：https://arxiv.org/abs/2008.00951
code：https://github.com/eladrich/pixel2style2pixel
project：https://eladrich.github.io/pixel2style2pixel/
人脸(Face)
[5] Cross Modal Focal Loss for RGBD Face Anti-Spoofing(Cross Modal Focal Loss for RGBD Face Anti-Spoofing)
paper：https://arxiv.org/abs/2103.00948
[4] When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework(当年龄不变的人脸识别遇到人脸年龄合成时：一个多任务学习框架)
paper：https://arxiv.org/abs/2103.01520
code：https://github.com/Hzzone/MTLFace
[3] Multi-attentional Deepfake Detection(多注意的深伪检测)
paper：https://arxiv.org/abs/2103.02406
[2] Image-to-image Translation via Hierarchical Style Disentanglement
paper：https://arxiv.org/abs/2103.01456
code：https://github.com/imlixinyang/HiSD
[1] A 3D GAN for Improved Large-pose Facial Recognition(用于改善大姿势面部识别的3D GAN)
paper：https://arxiv.org/pdf/2012.10545.pdf
目标跟踪(Object Tracking)
[4] HPS: localizing and tracking people in large 3D scenes from wearable sensors(通过可穿戴式传感器对大型3D场景中的人进行定位和跟踪)
[3] Track to Detect and Segment: An Online Multi-Object Tracker(跟踪检测和分段：在线多对象跟踪器)
project：https://jialianwu.com/projects/TraDeS.html
video：https://www.youtube.com/watch?v=oGNtSFHRZJA
[2] Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking(多目标跟踪的概率小波计分和修复)
paper：https://arxiv.org/abs/2012.02337
[1] Rotation Equivariant Siamese Networks for Tracking（旋转等距连体网络进行跟踪）
paper：https://arxiv.org/abs/2012.13078
重识别
[1] Meta Batch-Instance Normalization for Generalizable Person Re-Identification(通用批处理人员重新标识的元批实例规范化)
paper：https://arxiv.org/abs/2011.14670
医学影像(Medical Imaging)
[4] Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning(多机构协作改进基于深度学习的联合学习磁共振图像重建)
paper：https://arxiv.org/abs/2103.02148
code：https://github.com/guopengf/FLMRCM
[3] 3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management(用于胰腺肿块分割，诊断和定量患者管理的3D图形解剖学几何集成网络)
[2] Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies(深部病变追踪器：在4D纵向成像研究中监控病变)
paper：https://arxiv.org/abs/2012.04872
[1] Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization(通过脊柱矫正和解剖学约束优化在CT中自动进行椎骨定位和识别)
paper：https://arxiv.org/abs/2012.07947
神经网络架构搜索(NAS)
[3] AttentiveNAS: Improving Neural Architecture Search via Attentive(通过注意力改善神经架构搜索)
paper：https://arxiv.org/pdf/2011.09011.pdf
[2] ReNAS: Relativistic Evaluation of Neural Architecture Search(NAS predictor当中ranking loss的重要性)
paper：https://arxiv.org/pdf/1910.01523.pdf
[1] HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens（降低NAS的成本）
paper：https://arxiv.org/pdf/2005.14446.pdf
GAN/生成式/对抗式(GAN/Generative/Adversarial)
[5] Efficient Conditional GAN Transfer with Knowledge Propagation across Classes(高效的有条件GAN转移以及跨课程的知识传播)
paper：https://arxiv.org/abs/2102.06696
code：http://github.com/mshahbazi72/cGANTransfer
[4] Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing（利用GAN中潜在的空间维度进行实时图像编辑）
[3] Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs(Hijack-GAN：意外使用经过预训练的黑匣子GAN)
paper：https://arxiv.org/pdf/2011.14107.pdf
[2] Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation(样式编码：用于图像到图像翻译的StyleGAN编码器)
paper：https://arxiv.org/abs/2008.00951
code：https://github.com/eladrich/pixel2style2pixel
project：https://eladrich.github.io/pixel2style2pixel/
[1] A 3D GAN for Improved Large-pose Facial Recognition(用于改善大姿势面部识别的3D GAN)
paper：https://arxiv.org/pdf/2012.10545.pdf
三维视觉(3D Vision)
[2] A Deep Emulator for Secondary Motion of 3D Characters(三维角色二次运动的深度仿真器)
paper：https://arxiv.org/abs/2103.01261
[1] 3D CNNs with Adaptive Temporal Feature Resolutions(具有自适应时间特征分辨率的3D CNN)
paper：https://arxiv.org/abs/2011.08652
三维点云(3D Point Cloud)
[6] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市规模3D点云的语义分割：数据集，基准和挑战)
paper：https://arxiv.org/abs/2009.03137
code：https://github.com/QingyongHu/SensatUrban
[5] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration(SpinNet：学习用于3D点云注册的通用表面描述符)
paper：https://t.co/xIAWVGQeB2?amp=1
code：https://github.com/QingyongHu/SpinNet
[4] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通过3D扫描同步进行多主体分割和运动估计)
paper：https://arxiv.org/pdf/2101.06605.pdf
code：https://github.com/huangjh-pub/multibody-sync
[3] Diffusion Probabilistic Models for 3D Point Cloud Generation(三维点云生成的扩散概率模型)
paper：https://arxiv.org/abs/2103.01458
code：https://github.com/luost26/diffusion-point-cloud
[2] Style-based Point Generator with Adversarial Rendering for Point Cloud Completion(用于点云补全的对抗性渲染基于样式的点生成器)
paper：https://arxiv.org/abs/2103.02535
[1] PREDATOR: Registration of 3D Point Clouds with Low Overlap(预测器：低重叠的3D点云的注册)
paper：https://arxiv.org/pdf/2011.13005.pdf
code：https://github.com/ShengyuH/OverlapPredator
project：https://overlappredator.github.io/
三维重建(3D Reconstruction)
[1] PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers（具有透视作物层的3D姿势的几何感知神经重建）
paper：https://arxiv.org/abs/2011.13607
模型压缩(Model Compression)
[2] Manifold Regularized Dynamic Network Pruning（动态剪枝的过程中考虑样本复杂度与网络复杂度的约束）
[1] Learning Student Networks in the Wild（一种不需要原始训练数据的模型压缩和加速技术）
paper：https://arxiv.org/pdf/1904.01186.pdf
code：https://github.com/huawei-noah/DAFL
解读：
华为诺亚方舟实验室提出无需数据网络压缩技术：https://zhuanlan.zhihu.com/p/81277796
知识蒸馏(Knowledge Distillation)
[3] General Instance Distillation for Object Detection(通用实例蒸馏技术在目标检测中的应用)
paper:https://arxiv.org/abs/2103.02340
[2] Multiresolution Knowledge Distillation for Anomaly Detection(用于异常检测的多分辨率知识蒸馏)
paper:https://arxiv.org/abs/2011.11108
[1] Distilling Object Detectors via Decoupled Features（前景背景分离的蒸馏技术）
神经网络架构(Neural Network Structure)
[3] Rethinking Channel Dimensions for Efficient Model Design(重新考虑通道尺寸以进行有效的模型设计)
paper:https://arxiv.org/abs/2007.00992
code:https://github.com/clovaai/rexnet
[2] Inverting the Inherence of Convolution for Visual Recognition（颠倒卷积的固有性以进行视觉识别）
[1] RepVGG: Making VGG-style ConvNets Great Again
paper:https://arxiv.org/abs/2101.03697
code:https://github.com/megvii-model/RepVGG
解读：
RepVGG：极简架构，SOTA性能，让VGG式模型再次伟大:https://zhuanlan.zhihu.com/p/344324470
Transformer
[3] Transformer Interpretability Beyond Attention Visualization(注意力可视化之外的Transformer可解释性)
paper:https://arxiv.org/pdf/2012.09838.pdf
code:https://github.com/hila-chefer/Transformer-Explainability
[2] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
paper:https://arxiv.org/pdf/2011.09094.pdf
解读：无监督预训练检测器:https://www.zhihu.com/question/432321109/answer/1606004872
[1] Pre-Trained Image Processing Transformer(底层视觉预训练模型)
paper:https://arxiv.org/pdf/2012.00364.pdf
图神经网络(GNN)
[2] Quantifying Explainers of Graph Neural Networks in Computational Pathology(计算病理学中图神经网络的量化解释器)
paper:https://arxiv.org/pdf/2011.12646.pdf
[1] Sequential Graph Convolutional Network for Active Learning(主动学习的顺序图卷积网络)
paper:https://arxiv.org/pdf/2006.10219.pdf
数据处理(Data Processing)
数据增广(Data Augmentation)
[1] KeepAugment: A Simple Information-Preserving Data Augmentation(一种简单的保存信息的数据扩充)
paper:https://arxiv.org/pdf/2011.11778.pdf
归一化/正则化(Batch Normalization)
[3] Adaptive Consistency Regularization for Semi-Supervised Transfer Learning(半监督转移学习的自适应一致性正则化)
paper:https://arxiv.org/abs/2103.02193
code:https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning
[2] Meta Batch-Instance Normalization for Generalizable Person Re-Identification(通用批处理人员重新标识的元批实例规范化)
paper:https://arxiv.org/abs/2011.14670
[1] Representative Batch Normalization with Feature Calibration（具有特征校准功能的代表性批量归一化）
图像聚类(Image Clustering)
[2] Improving Unsupervised Image Clustering With Robust Learning（通过鲁棒学习改善无监督图像聚类）
paper:https://arxiv.org/abs/2012.11150
code:https://github.com/deu30303/RUC
[1] Reconsidering Representation Alignment for Multi-view Clustering(重新考虑多视图聚类的表示对齐方式)
模型评估(Model Evaluation)
[1] Are Labels Necessary for Classifier Accuracy Evaluation?(测试集没有标签，我们可以拿来测试模型吗？)
paper:https://arxiv.org/abs/2007.02915
解读:https://zhuanlan.zhihu.com/p/328686799
数据集(Database)
[2] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市规模3D点云的语义分割：数据集，基准和挑战)
paper:https://arxiv.org/abs/2009.03137
code:https://github.com/QingyongHu/SensatUrban
[1] Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels（重新标记ImageNet：从单标签到多标签，从全局标签到本地标签）
paper:https://arxiv.org/abs/2101.05022
code:https://github.com/naver-ai/relabel_imagenet
主动学习(Active Learning)
[3] Vab-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
paper:https://github.com/yuantn/MIAL/raw/master/paper.pdf
code:https://github.com/yuantn/MIAL
[2] Multiple Instance Active Learning for Object Detection（用于对象检测的多实例主动学习）
paper:https://github.com/yuantn/MIAL/raw/master/paper.pdf
code:https://github.com/yuantn/MIAL
[1] Sequential Graph Convolutional Network for Active Learning(主动学习的顺序图卷积网络)
paper:https://arxiv.org/pdf/2006.10219.pdf
小样本学习(Few-shot Learning)/零样本
[5] Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
paper:https://arxiv.org/abs/2012.06166
code:https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation
[4] Counterfactual Zero-Shot and Open-Set Visual Recognition(反事实零射和开集视觉识别)
paper:https://arxiv.org/abs/2103.00887
code:https://github.com/yue-zhongqi/gcm-cf
[3] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection(小样本目标检测的语义关系推理)
paper:https://arxiv.org/abs/2103.01903
[2] Few-shot Open-set Recognition by Transformation Consistency(转换一致性很少的开放集识别)
[1] Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning(探索少量学习的不变表示形式和等变表示形式的互补强度)
paper:https://arxiv.org/abs/2103.01315
持续学习(Continual Learning/Life-long Learning)
[2] Rainbow Memory: Continual Learning with a Memory of Diverse Samples（不断学习与多样本的记忆）
[1] Learning the Superpixel in a Non-iterative and Lifelong Manner(以非迭代和终身的方式学习超像素)
视觉推理(Visual Reasoning)
[1] Transformation Driven Visual Reasoning(转型驱动的视觉推理)
paper:https://arxiv.org/pdf/2011.13160.pdf
code:https://github.com/hughplay/TVR
project:https://hongxin2019.github.io/TVR/
迁移学习/domain/自适应](#domain)
[4] Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning(通过域随机化和元学习对视觉表示进行连续调整)
paper:https://arxiv.org/abs/2012.04324
[3] Domain Generalization via Inference-time Label-Preserving Target Projections(基于推理时间保标目标投影的区域泛化)
paper:https://arxiv.org/abs/2103.01134
[2] MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing(可伸缩的自适应视频压缩传感重建)
paper:https://arxiv.org/abs/2103.01786
code:https://github.com/xyvirtualgroup/MetaSCI-CVPR2021
[1] FSDR: Frequency Space Domain Randomization for Domain Generalization(用于域推广的频域随机化)
paper:https://arxiv.org/abs/2103.02370
对比学习(Contrastive Learning)
[1] Fine-grained Angular Contrastive Learning with Coarse Labels(粗标签的细粒度角度对比学习)
paper:https://arxiv.org/abs/2012.03515
暂无分类
Quantifying Explainers of Graph Neural Networks in Computational Pathology(计算病理学中图神经网络的量化解释器)
paper:https://arxiv.org/pdf/2011.12646.pdf
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts(探索具有对比场景上下文的数据高效3D场景理解)
paper:http://arxiv.org/abs/2012.09165
project:http://sekunde.github.io/project_efficient
video:http://youtu.be/E70xToZLgs4
Data-Free Model Extraction(无数据模型提取)
paper:https://arxiv.org/abs/2011.14779
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition(用于【位置识别】的局部全局描述符的【多尺度融合】)
paper:https://arxiv.org/pdf/2103.01486.pdf
code:https://github.com/QVPR/Patch-NetVLAD
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations(适用于正确概念的权利：通过可解释性来修正神经符号概念)
paper:https://arxiv.org/abs/2011.12854
Multi-Objective Interpolation Training for Robustness to Label Noise(多目标插值训练的鲁棒性)
paper:https://arxiv.org/abs/2012.04462
code:https://git.io/JI40X
VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs(【文本生成】VX2TEXT：基于视频的文本生成的端到端学习来自多模式输入)
paper:https://arxiv.org/pdf/2101.12059.pdf
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans(【图像字幕】Scan2Cap：RGB-D扫描中的上下文感知密集字幕)
paper:https://arxiv.org/abs/2012.02206
code:https://github.com/daveredrum/Scan2Cap
project:https://daveredrum.github.io/Scan2Cap/
video:https://youtu.be/AgmIpDbwTCY
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph(基于目标关系图的分层部分可观测目标驱动策略学习)
paper:https://arxiv.org/abs/2103.01350
ID-Unet: Iterative Soft and Hard Deformation for View Synthesis(视图合成的迭代软硬变形)
paper:https://arxiv.org/abs/2103.02264
PML: Progressive Margin Loss for Long-tailed Age Classification(【长尾分布】【图像分类】长尾年龄分类的累进边际损失)
paper:https://arxiv.org/abs/2103.02140
Diversifying Sample Generation for Data-Free Quantization（【图像生成】多样化的样本生成，实现无数据量化）
paper:https://arxiv.org/abs/2103.01049
Domain Generalization via Inference-time Label-Preserving Target Projections（通过保留推理时间的目标投影进行域泛化）
paper:https://arxiv.org/pdf/2103.01134.pdf
DeRF: Decomposed Radiance Fields（分解的辐射场）
project:https://ubc-vision.github.io/derf/
Densely connected multidilated convolutional networks for dense prediction tasks（【密集预测】密集连接的多重卷积网络，用于密集的预测任务）
paper:https://arxiv.org/abs/2011.11844
VirTex: Learning Visual Representations from Textual Annotations（【表示学习】从文本注释中学习视觉表示）
paper:https://arxiv.org/abs/2006.06666
code:https://github.com/kdexd/virtex
Weakly-supervised Grounded Visual Question Answering using Capsules（使用胶囊进行弱监督的地面视觉问答）
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation（【视频插帧】FLAVR：用于快速帧插值的与流无关的视频表示）
paper:https://arxiv.org/pdf/2012.08512.pdf
code:https://tarun005.github.io/FLAVR/Code
project:https://tarun005.github.io/FLAVR/
Probabilistic Embeddings for Cross-Modal Retrieval（跨模态检索的概率嵌入）
paper:https://arxiv.org/abs/2101.05068
Self-supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map(道路动力学和成本图的自监督式多步同时预测)
IIRC: Incremental Implicitly-Refined Classification(增量式隐式定义的分类)
paper:https://arxiv.org/abs/2012.12477
project:https://chandar-lab.github.io/IIRC/
Fair Attribute Classification through Latent Space De-biasing(通过潜在空间去偏的公平属性分类)
paper:https://arxiv.org/abs/2012.01469
code:https://github.com/princetonvisualai/gan-debiasing
project:https://princetonvisualai.github.io/gan-debiasing/
Information-Theoretic Segmentation by Inpainting Error Maximization(修复误差最大化的信息理论分割)
paper:https://arxiv.org/abs/2012.07287
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pretraining(【视频语言学习】UC2：通用跨语言跨模态视觉和语言预培训)
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling(通过稀疏采样进行视频和语言学习)
paper:https://arxiv.org/pdf/2102.06183.pdf
code:https://github.com/jayleicn/ClipBERT
D-NeRF: Neural Radiance Fields for Dynamic Scenes(D-NeRF：动态场景的神经辐射场)
paper:https://arxiv.org/abs/2011.13961
project:https://www.albertpumarola.com/research/D-NeRF/index.html
Weakly Supervised Learning of Rigid 3D Scene Flow(刚性3D场景流的弱监督学习)
paper:https://arxiv.org/pdf/2102.08945.pdf
code:https://arxiv.org/pdf/2102.08945.pdf
project:https://3dsceneflow.github.io/