Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images. However, this approach faces limitations when applied to scene-centric datasets, where multiple objects within an image are only implicitly captured in the global representation. Such global bootstrapping can lead to undesirable entanglement of object representations. Furthermore, even object-centric datasets stand to benefit from a finer-grained bootstrapping approach. In response to these challenges, we introduce a novel Cross-Image Object-Level Bootstrapping method tailored to enhance dense visual representation learning. By employing object-level nearest neighbor bootstrapping throughout the training, CrIBo emerges as a notably strong and adequate candidate for in-context learning, leveraging nearest neighbor retrieval at test time. CrIBo shows state-of-the-art performance on the latter task while being highly competitive in more standard downstream segmentation tasks. Our code and pretrained models are publicly available at https://github.com/tileb1/CrIBo.
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
The Hong Kong University of Science and Technology（香港科技大学）；The Shanghai AI Laboratory（上海人工智能实验室）；University of Cologne（科隆大学）；Qing Yuan Research Institute of Shanghai Jiao Tong University（上海交通大学清源研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
Qualcomm Technologies International GmbH（高通技术国际有限公司）；Arriver Software AB（Arriver软件公司）；Linköping University（林雪平大学）；Qualcomm SARL（高通法国公司）；Qualcomm Technologies, Inc（高通技术公司）；KTH Royal Institute of Technology（皇家理工学院（瑞典））
Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Shanghai Institute of Intelligent Science and Technology, Tongji University（同济大学上海智能科学与技术研究院）；School of Management, Hefei University of Technology（合肥工业大学管理学院）
Bootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Counting
College of Electronic and Information Engineering, Tongji University（电子信息工程学院，同济大学）；College of Computer Science, Wuhan University（计算机科学学院，武汉大学）；College of Computer Science, Tongji University（计算机科学学院，同济大学）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）
Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；Centre for Vision, Speech and Signal Processing（视觉、语音与信号处理中心）；University of Surrey（萨里大学）
Point2RBox-v3: Self-Bootstrapping from Point Annotations via Integrated Pseudo-Label Refinement and Utilization
Shanghai Jiao Tong University（上海交通大学）；South China University of Technology（华南理工大学）；Nankai University（南开大学）；The Chinese University of Hong Kong（香港中文大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；East China Normal University（华东师范大学）；Ohio State University（俄亥俄州立大学）
Data-Centric Recursive Self-Improvement for Navigation via Policy-Synchronized Language-Action Dual Bootstrapping
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；University of Adelaide（阿德莱德大学）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；Nanjing University（南京大学）；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）；Fudan University（复旦大学）
Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics
Institute for Language, Cognition and Computation, University of Edinburgh（语言、认知与计算研究所，爱丁堡大学）；Language Technology Lab, University of Cambridge（语言技术实验室，剑桥大学）；NVIDIA（NVIDIA公司）；University of Groningen（格罗宁根大学）
