Query Re-Identification

Latest papers

9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

ReForesight: Occlusion-Robust Re-Identification and Future Occupancy Forecasting for Dynamic Semantic Mapping
Oct 12, 2026Didar Seyidov, Faith Ogunwoye, Hajira Amjad +3Query Re-Identification Trajectory Prediction
Skolkovo Institute of Science and Technology, Moscow, Russia · 168robotics, Moscow, Russia
DiffReID: Discriminative Diffusion Model for Object Re-Identification
Sep 29, 2026Yingquan Wang, Pingping Zhang, Dong Wang +1Representation Learning Query Re-Identification
School of Information and Communication Engineering, Dalian University of Technology · School of Future Technology, Dalian University of Technology
FM-ReID: Selective Competitive Token Routing for Object Re-Identification
Sep 29, 2026Zhiqi Li, Xiaowei Zhou, Zeyuan Sun +2Fine-Grained Image Retrieval Query Re-Identification
Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China · Sanya Oceanographic Institution, Ocean University of China, Sanya, China
VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects
Jul 19, 2026Yanrong Qin, Xiaoyan Cao, Yao YaoQuery Re-Identification Data Association
Glasgow College, University of Electronic Science and Technology of China, Chengdu, China · Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, China · School of Management Science and Real Estate, Chongqing University, Chongqing, China
Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles
Jul 6, 2026Soren Antebi, Stefan Eickeler, Sandra Halscheidt +4Image Matching Industrial Inspection
Fraunhofer Institute IAIS, Sankt Augustin, Germany · Rathscheck Schiefer GmbH, Mayen, Germany
SAMOFT: Robust Multi-Object Tracking via Region and Flow
May 10, 2026Yanchao Wang, Dawei Zhang, Chengzhuan Yang +5Query Re-Identification Multi-Object Tracking
School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China · School of Automation and Intelligent Sensing, the Institute of Image Processing and Pattern Recognition, and the Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China · Institute for Sustainable Industries and Liveable Cities, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia +1
HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
Apr 22, 2026Peng Peng, Weiwei Lin, Wentai Wu +2Retrieval-Augmented Generation Query Re-Identification
South China University of Technology Guangzhou, China · Pengcheng Laboratory Shenzhen, China · Jinan University Guangzhou, China +1
Re-Prompting SAM 3 via Object Retrieval: 3rd of the 5th PVUW MOSE Track
Mar 24, 2026Mingqi Gao, Sijie Li, Jungong HanQuery Re-Identification Video Object Segmentation
School of Computer Science, University of Sheffield · Department of Automation, Tsinghua University
AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos
Mar 8, 2026Teng Yan, Yihan Liu, Jiongxu Chen +3Query Re-Identification Vision-Language Grounding
The Hong Kong University of Science and Technology (Guangzhou)

Oct 12, 2026J/K move · Enter open · S save

ReForesight: Occlusion-Robust Re-Identification and Future Occupancy Forecasting for Dynamic Semantic Mapping

Didar Seyidov, Faith Ogunwoye, Hajira Amjad, Danil Tolkachev+2

Skolkovo Institute of Science and Technology, Moscow, Russia · 168robotics, Moscow, Russia

Query Re-Identification Trajectory Prediction 3D Occupancy Prediction Pedestrian Trajectory Prediction Semantic Occupancy Mapping 3D MOT

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Robots sharing walkways need to know where people will be, yet semantic occupancy maps show only where they are. We present ReForesight, an open-source ROS 2 toolkit that adds instance-level future occupancy and re-identification after occlusion to a particle-based semantic mapping pipeline. A 36.6k-parameter MLP predicts each tracked person or vehicle up to 3 s ahead, guarded by a Kalman fallback and a stationary gate, and the object's own points are moved to the forecast pose, forming a "ghost" point cloud in the map frame without an occupancy decoder. An optional tracker re-associates objects after occlusion using keypoint descriptors the pipeline already computes and a metric 3D gate. On four held-out CODa sequences, the MLP lowers the mean 3 s final displacement error from 0.728 m (Kalman) to 0.662 m and beats a similar-size GRU (0.692 m) on every sequence, at 0.04 ms per instance on one CPU core. Code, all models, and a ROS-free demo are released; field demonstrations use an outdoor cleaning robot.