Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline, has achieved promising performance. However, it requires large-scale labeled data and suffers from domain shift, especially when no labeled data is available in the target domain. To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels. We further propose the comprehensive multi-level feature alignment to improve the pseudo labels generated by the mean teacher framework taking advantage of the cross-scale self-attention mechanism in Deformable DETR. Image and object features are aligned at the local, global, and instance levels with domain query-based feature alignment (DQFA), bi-level graph-based prototype alignment (BGPA), and token-wise image feature alignment (TIFA). On the other hand, the unlabeled target domain data pseudo-labeled and available for the object detection training by the mean teacher framework can lead to better feature extraction and alignment. Thus, the mean teacher framework and the comprehensive multi-level feature alignment can be optimized iteratively and mutually based on the architecture of Transformers. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance in three domain adaptation scenarios, especially the result of Sim10k to Cityscapes scenario is remarkably improved from 52.6 mAP to 57.9 mAP. Code will be released.
Knowledge Amalgamation for Object Detection with Transformers
College of Computer Science, Zhejiang university（浙江大学计算机学院）；Alibaba（阿里巴巴）；College of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Lab（之江实验室）
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）；Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University（暨南大学广东省中医信息技术重点实验室）；Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究学院（ICREA））
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
Université Paris-Saclay, CEA, LIST（巴黎-萨克雷大学，法国原子能委员会，信息系统数字技术实验室）；Université de Lyon, UJM-Saint-Etienne, CNRS, IOGS（里昂大学，圣艾蒂安让·莫内大学，法国国家科学研究中心，光学研究所）；Laboratoire Hubert Curien UMR 5516（于贝尔·居里安实验室 UMR 5516）；Institut Universitaire de France (IUF)（法国大学研究院）
Weakly Supervised Point Clouds Transformer for 3D Object Detection
College of Software Engineering, Southeast University（东南大学软件学院）；Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences（中国科学院海西研究院泉州装备制造研究所）；College of Mechanical Engineering, Xinjiang University（新疆大学机械工程学院）
General Compression Framework for Efficient Transformer Object Tracking
Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University（上海智能信息处理关键实验室，计算机科学与人工智能学院，复旦大学）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；Lenovo Research（联想研究）
Attention Transfer Is Not Universally Effective for Vision Transformers
Institute for Infocomm Research (I 2 R), A*STAR, Singapore（信息与通信研究 institute（I 2 R），A*STAR，新加坡）；Sichuan University（四川大学）；Shanghai Jiao Tong University（上海交通大学）
South China University of Technology（华南理工大学）；MIT（麻省理工学院）；University of Texas at Austin（得克萨斯大学奥斯汀分校）；Tsinghua University（清华大学）；Shanghai Qi Zhi Institute（上海期智研究院）
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
University of Bucharest（布加勒斯特大学）；Norwegian University of Science and Technology（挪威科技大学）；University of Medicine and Pharmacy “Carol Davila”（卡罗尔·达维拉医药大学）
Distilling Vision Transformers for Distortion-Robust Representation Learning
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece（希腊雅典国家与卡波迪斯蒂亚纳大学信息与电信系）；National Observatory of Athens, Greece（希腊雅典国家天文台）；Information Management Systems Institute, Athena Research Center, Greece（希腊阿提卡研究中心信息管理系统研究所）
Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking
Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（教育区块链与智能技术重点实验室，教育部，广西师范大学）；Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University（广西多源信息挖掘与安全重点实验室，广西师范大学）；Nanjing University of Science and Technology（南京理工大学）；Xi’an Research Institute of High Technology（西安高新技术研究所）
MaTVLM: Hybrid Mamba-Transformer for Efficient Vision-Language Modeling
School of EIC, Huazhong University of Science & Technology（华中科技大学电子信息与通信学院）；Institute of Artificial Intelligence, Huazhong University of Science & Technology（华中科技大学人工智能研究院）
A Graph-Augmented knowledge Distillation based Dual-Stream Vision Transformer with Region-Aware Attention for Gastrointestinal Disease Classification with Explainable AI
Department of Computer Science and Engineering（计算机科学与工程系）
X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；The Future Network of Intelligence Institute（未来网络智能研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shanghai Jiao Tong University（上海交通大学）；Sun Yat-sen University（中山大学）
Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking
College of Computer Science and Engineering, Guilin University of Technology（桂林理工大学计算机科学与工程学院）；Guangxi Key Laboratory of Embedded Technology and Intelligent Information Processing（广西嵌入式技术与智能信息处理重点实验室）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；College of Computer Science, Sichuan University（四川大学计算机科学学院）
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Ant Financial Service Group（蚂蚁金融服务集团）；PeopleAI, Inc.（PeopleAI公司）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
