The emerging field of action prediction plays a vital role in various computer vision applications such as autonomous driving, activity analysis and human-computer interaction. Despite significant advancements, accurately predicting future actions remains a challenging problem due to high dimensionality, complex dynamics and uncertainties inherent in video data. Traditional supervised approaches require large amounts of labelled data, which is expensive and time-consuming to obtain. This paper introduces a novel self-supervised video strategy for enhancing action prediction inspired by DINO (self-distillation with no labels). The Temporal-DINO approach employs two models; a 'student' processing past frames; and a 'teacher' processing both past and future frames, enabling a broader temporal context. During training, the teacher guides the student to learn future context by only observing past frames. The strategy is evaluated on ROAD dataset for the action prediction downstream task using 3D-ResNet, Transformer, and LSTM architectures. The experimental results showcase significant improvements in prediction performance across these architectures, with our method achieving an average enhancement of 9.9% Precision Points (PP), highlighting its effectiveness in enhancing the backbones' capabilities of capturing long-term dependencies. Furthermore, our approach demonstrates efficiency regarding the pretraining dataset size and the number of epochs required. This method overcomes limitations present in other approaches, including considering various backbone architectures, addressing multiple prediction horizons, reducing reliance on hand-crafted augmentations, and streamlining the pretraining process into a single stage. These findings highlight the potential of our approach in diverse video-based tasks such as activity recognition, motion planning, and scene understanding.
Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving
Technical University of Munich（慕尼黑技术大学）；Huawei Hilbert Research Center（华为希利伯特研究中心）；Huawei Riemann Lab（华为里曼实验室）；Wuhan University（武汉大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）
DIVE: Taming DINO for Subject-Driven Video Editing
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；vivo AI Lab（vivo AI实验室）；NVIDIA；Adobe Research（Adobe研究院）；Shenzhen University（深圳大学）；Southeast University（东南大学）；Shenzhen University of Advanced Technology（深圳大学先进技术学院）
DINO Pre-training for Vision-based End-to-end Autonomous Driving
Vilnius University Institute of Data Science and Digital Technologies（维尔纽斯大学数据科学与数字技术研究所）；Research Institute of Natural and Technological Sciences, Vytautas Magnus University（维陶塔斯马格努斯大学自然与技术科学研究所）
Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis
Herman Ostrow School of Dentistry, University of Southern California（南加州大学赫尔曼·奥斯特罗牙科学院）；Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院）
DINOv3-Diffusion Policy: Self-Supervised Large Visual Model for Visuomotor Diffusion Policy Learning
Department of Computing and Mathematics, Manchester Metropolitan University（计算与数学系，曼彻斯特 Metropolitan 大学）；Department of Computing, Imperial College London（计算系，帝国理工学院伦敦分校）
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting
Stadtspital Zürich（苏黎世市立医院）；Spross Research Institute（斯普罗斯研究所）；Gutblick Research（古特布里克研究公司）；Balgrist University Hospital（巴尔格里斯特大学医院）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院国民保健服务基金会信托）；Institute of Ophthalmology, University College London（伦敦大学学院眼科研究所）；University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国家眼科中心新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；iScreen 2 Prevent LLC（iScreen 2 Prevent有限责任公司）；Bascom Palmer Eye Institute, University of Miami（迈阿密大学巴斯康帕默眼科研究所）；University of Heidelberg（海德堡大学）；Semmelweis University（塞梅尔魏斯大学）
Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images
Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India（电子与通信工程系，Manipal理工学院，Manipal高等教育学院，Manipal，印度）
Self-supervised Pretraining of Cell Segmentation Models
School of Computer Science, Georgia Institute of Technology（佐治亚理工学院计算机科学学院）；School of Materials Science and Engineering, Georgia Institute of Technology（佐治亚理工学院材料科学与工程学院）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院机械工程学院）
