转载自CVPR 2022 全面盘点:最新350篇论文总结 / 代码 / 解读 / 直播 / 项目(更新) - 知乎
CVPR 2022 已上市，共收到2067篇论文，收到的论文数量比去年增加了24%。CVPR在2022年正式会议之前，为了让大家更快地获得和学习计算机视觉前沿技术，极市对CVPR跟踪022 最新论文，包括以及。
官网链接：http://CVPR2022.thecvf.com 会议时间：2021年6月19日至6月24日 相关问题:如何评价 CVPR2022 论文接收结果？ 相关报道:CVPR 2022 接收结果公布！雇佣 2067 ，接收次数增加24%
此前我们对CVPR2021/CVPR整理了2020/2019/2018，所有内容都总结在我们身上Github：
https://github.com/extreme-assistant/CVPR2022-Paper-Code-Interpretation
github.com/extreme-assistant/CVPR2022-Paper-Code-Interpretation
update:
1. CVPR2022年 接受论文/代码分方向汇总(更新) 2. CVPR2022 Oral（更新中） 3. CVPR2022 论文解读总结(更新) 4. CVPR分享2022年 极市论文 5. To do list
[12] Progressive End-to-End Object Detection in Crowded Scenes(在拥挤场景中逐步端到端对象检测)paper | code
[11] Real-time Object Detection for Streaming Perception(实时检测流感知对象)paper | code
[10] Oriented RepPoints for Aerial Object Detection(向空中目标检测 RepPoints)()paper | code
[9] Confidence Propagation Cluster: Unleash Full Potential of Object Detectors(信心传播集群:释放物体探测器的所有潜力)paper
[8] Semantic-aligned Fusion Transformer for One-shot Object Detection(语义对齐融合转换器用于一次性目标检测)paper
[7] A Dual Weighting Label Assignment Scheme for Object Detection(目标检测双重加权标签分配方案)paper | code
[6] MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection(混合图像块和 UnMix 用于半监督目标检测的特征块)paper | code
[5] SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection(域自适应对象检测的语义完全图匹配)paper | code
[4] Accelerating DETR Convergence via Semantic-Aligned Matching(通过语义对齐匹配加速 DETR 收敛)paper | code
[3] Focal and Global Knowledge Distillation for Detectors(蒸馏探测器的焦点和全球知识) keywords: Object Detection,Knowledge Distillationpaper | code
[2] Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild(未知感知对象检测:从野外视频中学习你不知道的东西)paper | code
[1] Localization Distillation for Dense Object Detection(定位蒸馏密集对象检测) keywords: Bounding Box Regression,Localization Quality Estimation,Knowledge Distillationpaper | code 解读:明明团队和天大提出南开程LD：目标检测定位蒸馏
[1] Unsupervised Activity Segmentation by Joint Representation Learning and Online Clustering(无监督活动通过联合表示学习和在线聚类划分)paper | video
[13] TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers(用于 3D 对象检测稳定 LiDAR-Camera Fusion 与 Transformer)paper | code
[12] Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds(学习用于 3D LiDAR 基于点的高效点云检测器)paper | code
[11] Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion(走向高质量的 3，深度完成D 检测)paper
[10] MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer(使用深度感知 Transformer 的单目 3D 对象检测)paper | code
[9] Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds(从点云进行 3D 对象检测 Set-to-Set 方法)paper | code
[8] VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attentionpaper | code
[7] MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection(单目 3D 目标检测的联合语义和几何成本)paper | code
[6] DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection(用于多模态 3D 目标激光雷达相机深度集成)paper | code
[5] Point Density-Aware Voxels for LiDAR 3D Object Detection(用于 LiDAR 3D 对象检测的点密度感知元素)paper | code
[4] Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement(带有形状引导标签增强的弱监督 3D 对象检测)paper | code
[3] Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes(在 3D 场景中实现稳健的定向边界框检测)paper | code
[2] A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation(在全景分割的指导下，用于基于 LiDAR 的 3D 对象检测的多功能多视图框架) keywords: 3D Object Detection with Point-based Methods, 3D Object Detection with Grid-based Methods, Cluster-free 3D Panoptic Segmentation, CenterPoint 3D Object Detectionpaper
[1] Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving(自动驾驶中用于单目 3D 目标检测的伪立体) keywords: Autonomous Driving, Monocular 3D Object Detectionpaper | code
[2] Implicit Motion Handling for Video Camouflaged Object Detection(视频伪装对象检测的隐式运动处理)paper | dataset
[1] Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection(放大和缩小：用于伪装目标检测的混合尺度三元组网络)paper | code
[2] Bi-directional Object-context Prioritization Learning for Saliency Ranking(显着性排名的双向对象上下文优先级学习)paper | code
[1] Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection()paper
[1] UKPGAN: A General Self-Supervised Keypoint Detector(一个通用的自监督关键点检测器)paper | code
[2] CLRNet: Cross Layer Refinement Network for Lane Detection(用于车道检测的跨层细化网络)paper
[1] Rethinking Efficient Lane Detection via Curve Modeling(通过曲线建模重新思考高效车道检测) keywords: Segmentation-based Lane Detection, Point Detection-based Lane Detection, Curve-based Lane Detection, autonomous drivingpaper | code
[1] EDTER: Edge Detection with Transformer(使用transformer的边缘检测)paper | code
[1] Deep vanishing point detection: Geometric priors make dataset variationish(深度消失点检测：几何先验使数据集变化消失)paper | code
[4] UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection(监督开放集视频异常检测的新基准)paper | code
[3] ViM: Out-Of-Distribution with Virtual-logit Matching(具有虚拟 logit 匹配的分布外)()paper | code
[2] Generative Cooperative Learning for Unsupervised Video Anomaly Detection(用于无监督视频异常检测的生成式协作学习)paper
[1] Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection(用于异常检测的自监督预测卷积注意力块)(论文暂未上传)paper | code
[3] Learning What Not to Segment: A New Perspective on Few-Shot Segmentation(学习不分割的内容：关于小样本分割的新视角)paper | code
[2] CRIS: CLIP-Driven Referring Image Segmentation(CLIP 驱动的参考图像分割)paper
[1] Hyperbolic Image Segmentation(双曲线图像分割)paper
[2] Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers(使用 Transformers 深入研究全景分割)paper | code
[1] Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation(弯曲现实：适应全景语义分割的失真感知Transformer) keywords: Semantic- and panoramic segmentation, Unsupervised domain adaptation, Transformerpaper | code
[16] DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation(用于语义变化分割的每日多光谱卫星数据集)paper | data | website
[15] Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation(用于域自适应语义分割的类平衡像素级自标记)paper | code
[14] Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation(弱监督语义分割的区域语义对比和聚合)paper | code
[13] Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation(走向稀疏注释的语义分割)paper | code
[12] Scribble-Supervised LiDAR Semantic Segmentationpaper |code
[11] ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation(多目标域自适应语义分割的直接适应策略)paper
[10] Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast(通过像素到原型对比的弱监督语义分割)paper
[9] Representation Compensation Networks for Continual Semantic Segmentation(连续语义分割的表示补偿网络)paper | code
[8] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels(使用不可靠伪标签的半监督语义分割)paper | code | project
[7] Weakly Supervised Semantic Segmentation using Out-of-Distribution Data(使用分布外数据的弱监督语义分割)paper | code
[6] Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation(弱监督语义分割的自监督图像特定原型探索)paper | code
[5] Multi-class Token Transformer for Weakly Supervised Semantic Segmentation(用于弱监督语义分割的多类token Transformer)paper | code
[4] Cross Language Image Matching for Weakly Supervised Semantic Segmentation(用于弱监督语义分割的跨语言图像匹配)paper
[3] Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers(从注意力中学习亲和力：使用 Transformers 的端到端弱监督语义分割)paper | code
[2] ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation(让自我训练更好地用于半监督语义分割) keywords: Semi-supervised learning, Semantic segmentation, Uncertainty estimationpaper | code
[1] Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation(弱监督语义分割的类重新激活图)paper | code
[6] Mask Transfiner for High-Quality Instance Segmentation(用于高质量实例分割的 Mask Transfiner)paper | code
[5] ContrastMask: Contrastive Learning to Segment Every Thing(对比学习分割每件事)paper
[4] Discovering Objects that Can Move(发现可以移动的物体)paper | code
[3] E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation(一种基于端到端轮廓的高质量高速实例分割方法)paper | code
[2] Efficient Video Instance Segmentation via Tracklet Query and Proposal(通过 Tracklet Query 和 Proposal 进行高效的视频实例分割)paper
[1] SoftGroup for 3D Instance Segmentation on Point Clouds(用于点云上的 3D 实�