Self-supervised pre-training and transformer-based networks have significantly improved the performance of object detection. However, most of the current self-supervised object detection methods are built on convolutional-based architectures. We believe that the transformers' sequence characteristics should be considered when designing a transformer-based self-supervised method for the object detection task. To this end, we propose SeqCo-DETR, a novel Sequence Consistency-based self-supervised method for object DEtection with TRansformers. SeqCo-DETR defines a simple but effective pretext by minimizes the discrepancy of the output sequences of transformers with different image views as input and leverages bipartite matching to find the most relevant sequence pairs to improve the sequence-level self-supervised representation learning performance. Furthermore, we provide a mask-based augmentation strategy incorporated with the sequence consistency strategy to extract more representative contextual information about the object for the object detection task. Our method achieves state-of-the-art results on MS COCO (45.8 AP) and PASCAL VOC (64.1 AP), demonstrating the effectiveness of our approach.
PaQ-DETR: Learning Pattern and Quality-Aware Dynamic Queries for Object Detection
New York University（纽约大学）；Boise State University（博伊西州立大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Irvine（加州大学 Irvine 分校）；Illinois Institute of Technology（伊利诺伊理工学院）；University of Pittsburgh（匹兹堡大学）
University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；SenseTime Research（商汤科技研究院）
3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Due to its low cost and high efficiency, multi-view 3D object detection has demonstrated promising application prospects. However, accurately detecting objects through perspective views in the 3D space is extremely difficult due to the lack of depth information. Recently, DETR3D introduces a novel 3D-2D query paradigm in aggregating multi-view images for 3D object detection and achieves state-of-the-art performance. In this paper, with intensive pilot experiments, we quantify the…
National University of Singapore（新加坡国立大学）；Harbin Institute of Technology（哈尔滨工业大学）
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision. Furthermore, the exact classes of the unknown objects must be identified without catastrophic forgetting of the previous known classes when the corresponding annotations of unknown objects are given incrementally. In this paper, we propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. In the first stage, we pre-train a model on the current annotated data to detect objects…
Seoul National University（首尔国立大学）；SK Telecom（SK电讯）
Semi-supervised Learning (SSL) has received increasing attention in autonomous driving to reduce the enormous burden of 3D annotation. In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level point cloud: learning from unlabeled de-identified intermediate features (i.e., smashed data) to preserve privacy. Since these intermediate features are naturally produced by the inference pipeline, no additional computation is required on autonomous vehicles. However, generating effective consistency loss for unlabeled feature-level scene turns out…
3D object detection with surrounding cameras has been a promising direction for autonomous driving. In this paper, we present SimMOD, a Simple baseline for Multi-camera Object Detection, to solve the problem. To incorporate multi-view information as well as build upon previous efforts on monocular 3D object detection, the framework is built on sample-wise object proposals and designed to work in a two-stage manner. First, we extract multi-scale features and generate the perspective object proposals on each monocular image. Second, the multi-view proposals are aggregated and then iteratively re…
DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Key Research Center for Surface Monitoring and Analysis of Relics, State Administration of Cultural Heritage（国家文物局文物表面监测与分析重点研究中心）；Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology（深圳理工大学计算机科学与人工智能学院）
CGSA: Class-Guided Slot-Aware Adaptation for Source-Free Object Detection
School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；Department of Electrical & Computer Engineering, National University of Singapore（电气与计算机工程系，新加坡国立大学）；School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）
Butter: Frequency Consistency and Hierarchical Fusion for Autonomous Driving Object Detection
Tsinghua University（清华大学）；University of Chinese Academy of Sciences（中国科学院大学）；Southeast University（东南大学）；University of Liverpool（利物浦大学）；Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, France（斯特拉斯堡大学、法国国家科学研究中心、法国国家医学研究院、ICube、UMR7357、法国）；University Hospital of Strasbourg, France（斯特拉斯堡大学医院、法国）
