ECCV2022论文汇总：检测/分割/跟踪/3D/深度估计/姿态解算等多个方向！

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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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