作者 | 汽车人
编辑 | Autobox
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数据集
COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts
地址：https://arxiv.org/pdf/2207.04675
Github：https://github.com/ku21fan/COO-Comic-Onomatopoeia
视觉transformer
[1].k-means Mask Transformer
论文链接: http://arxiv.org/pdf/2207.04044
代码链接: https://github.com/google-research/deeplab2
[2].Weakly Supervised Grounding for VQA in Vision-Language Transformers
论文链接: http://arxiv.org/pdf/2207.02334
代码链接: https://github.com/aurooj/wsg-vqa-vltransformers
[3].Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning
论文链接: http://arxiv.org/pdf/2207.04978
代码链接: https://github.com/YehLi/ImageNetModel
[4].CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition
论文链接: http://arxiv.org/pdf/2207.04410
代码链接: https://github.com/Green-Wood/CoMER
[5].MaxViT: Multi-Axis Vision Transformer
论文介绍了一种高效且可扩展的注意力模型，称之为多轴注意力
论文链接：MaxViT: Multi-Axis Vision Transformer
[6].V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer.
主页链接：https://github.com/DerrickXuNu/v2x-vit
目标检测
[1].ObjectBox: From Centers to Boxes for Anchor-Free Object Detection
Anchor-free模型新范式；
论文链接：ObjectBox: From Centers to Boxes for Anchor-Free Object Detection
代码链接：https://github.com/MohsenZand/ObjectBox
[2].Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
半监督目标检测方法：Dense Teacher
论文链接: http://arxiv.org/pdf/2207.02541
[3]Should All Proposals be Treated Equally in Object Detection?
目标检测中的采样讨论！
论文链接: http://arxiv.org/pdf/2207.03520
[4].ViTDet: Exploring Plain Vision Transformer Backbones for Object Detection.
来自何凯明团队，证明了将普通的、非分层的视觉 Transformer 作为主干网络进行目标检测的可行性；
论文链接：Exploring Plain Vision Transformer Backbones for Object Detection
代码链接：https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet
目标跟踪
[1].Towards Grand Unification of Object Tracking 多任务统一框架Unicorn，解决四个跟踪问题（SOT、MOT、VOS、MOTS）。Unicorn 在 8 个跟踪数据集（包括 LaSOT、TrackingNet、MOT17、BDD100K、DAVIS16-17、MOTS20 和 BDD100K MOTS）中的表现与其特定任务的counterparts相当或更好。
论文链接：Towards Grand Unification of Object Tracking
代码链接：https://github.com/MasterBin-IIAU/Unicorn
[2].MOTR: End-to-End Multiple-Object Tracking with TRansformer
论文链接：https://arxiv.org/pdf/2105.03247.pdf
代码链接：https://github.com/megvii-research/MOTR
3D检测
[1].FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection
全卷积anchor-free方案3D目标检测；
论文链接：FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection
代码链接：https://github.com/SamsungLabs/fcaf3d
[2].PETR: Position Embedding Transformation for Multi-View 3D Object Detection
论文链接：PETR: Position Embedding Transformation for Multi-View 3D Object Detection
代码链接：https://github.com/megvii-research/PETR
[3].AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
多模态3D目标检测
[1].论文链接：AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
语义分割
[1].Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
论文链接: http://arxiv.org/pdf/2207.02372
代码链接: https://github.com/xing0047/tps
[2].OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers
论文链接: http://arxiv.org/pdf/2207.02255
代码链接: https://github.com/pjlallen/osformer
[3].Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation.
无监督域适应方法
论文链接：https://arxiv.org/pdf/2207.06654.pdf
代码链接：https://github.com/jiangzhengkai/ProCA
[4].2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds 2D先验辅助的激光雷达点云语义分割
论文链接：2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
代码链接：https://github.com/yanx27/2dpass
车道线检测
[1].PersFormer: a New Baseline for 3D Laneline Detection.
3D车道线检测新基线；
论文链接：PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
代码链接：https://github.com/OpenPerceptionX/PersFormer_3DLane
点云相关
[1].Open-world Semantic Segmentation for LIDAR Point Clouds
论文链接: http://arxiv.org/pdf/2207.01452
代码链接:
https://github.com/jun-cen/open_world_3d_semantic_segmentation
[2].2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
论文链接: http://arxiv.org/pdf/2207.04397
深度估计
[1].Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
论文链接: http://arxiv.org/pdf/2207.04718
[2].Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics
论文链接: http://arxiv.org/pdf/2207.04680
6D姿态估计
[1].Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images.
论文链接：Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
代码链接：Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
其它
[1].MVSTER: Epipolar Transformer for Efficient Multi-View Stereo.
论文链接：MVSTER: Epipolar Transformer for Efficient Multi-View Stereo
代码链接：https://github.com/JeffWang987/MVSTER
Workshop竞赛汇总
[1].Visual Inductive Priors for Data-Efficient Computer Vision
链接：https://vipriors.github.io/challenges/
[2].ECCV'22 ChaLearn Seasons in Drift Challenge
链接：https://codalab.lisn.upsaclay.fr/competitions/4272
[3].ECCV'22 ChaLearn Seasons in Drift Challenge
链接：https://codalab.lisn.upsaclay.fr/competitions/4273
[4].ECCV'22 ChaLearn Seasons in Drift Challenge
链接：https://codalab.lisn.upsaclay.fr/competitions/4276
[5].ECCV'22 ChaLearn Sign Spotting Challenge Challenge
链接：https://codalab.lisn.upsaclay.fr/competitions/4198
[6].ECCV'22 ChaLearn Sign Spotting Challenge Challenge
链接：https://codalab.lisn.upsaclay.fr/competitions/4199
[7].ECCV DeeperAction Challenge – MultiSports Track on Human Action Detection
链接：https://codalab.lisn.upsaclay.fr/competitions/3736
[8].ECCV DeeperAction Challenge – FineAction Track on Temporal Action Localization
链接：https://codalab.lisn.upsaclay.fr/competitions/4386
[9].ECCV DeeperAction Challenge – UrbanPipe Track on Fine-grained Video Anomaly Recognition.
链接：https://codalab.lisn.upsaclay.fr/competitions/4303
[10].ECCV 2022 WCPA Challenge: From Face, Body and Fashion to 3D Virtual Avatars Ⅰ
链接：
https://tianchi.aliyun.com/competition/entrance/531958/introduction?spm=5176.12281957.1004.3.2fe13eafY89Q32
[11].ECCV 2022 WCPA Challenge: From Face, Body and Fashion to 3D Virtual Avatars Ⅱ
链接：
https://tianchi.aliyun.com/competition/entrance/531961/introduction?spm=5176.12281957.1004.2.2fe13eafY89Q32
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