{"total":19,"rows":[{"id":"2104.00194","title":"TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking","created":"2021-04-01","cited_by_count":93,"area":null},{"id":"2103.15145","title":"TransCenter: Transformers with Dense Representations for Multiple-Object Tracking","created":"2021-03-28","cited_by_count":42,"area":null},{"id":"2203.14208","title":"Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking","created":"2022-03-27","cited_by_count":33,"area":null},{"id":"2111.05943","title":"Self-Supervised Multi-Object Tracking with Cross-Input Consistency","created":"2021-11-10","cited_by_count":25,"area":null},{"id":"2202.08837","title":"Adiabatic Quantum Computing for Multi Object Tracking","created":"2022-02-17","cited_by_count":22,"area":null},{"id":"2211.02175","title":"Large Scale Real-World Multi-Person Tracking","created":"2022-11-03","cited_by_count":14,"area":null},{"id":"2110.07790","title":"DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception","created":"2021-10-15","cited_by_count":10,"area":null},{"id":"2311.00987","title":"CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation","created":"2023-11-02","cited_by_count":9,"area":null},{"id":"2205.10766","title":"Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey","created":"2022-05-22","cited_by_count":8,"area":null},{"id":"2404.11426","title":"SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow","created":"2024-04-17","cited_by_count":7,"area":null},{"id":"2108.03821","title":"Video Annotation for Visual Tracking via Selection and Refinement","created":"2021-08-09","cited_by_count":5,"area":null},{"id":"2208.02580","title":"SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset","created":"2022-08-04","cited_by_count":3,"area":null},{"id":"2306.09613","title":"UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation","created":"2023-06-16","cited_by_count":1,"area":null},{"id":"2312.04167","title":"Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation","created":"2023-12-07","cited_by_count":1,"area":null},{"id":"2104.02749","title":"Heuristics2Annotate: Efficient Annotation of Large-Scale Marathon Dataset For Bounding Box Regression","created":"2021-04-06","cited_by_count":0,"area":null},{"id":"2202.09315","title":"Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder","created":"2022-02-18","cited_by_count":0,"area":null},{"id":"2309.01078","title":"UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance","created":"2023-09-03","cited_by_count":0,"area":null},{"id":"2405.03643","title":"Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors","created":"2024-05-06","cited_by_count":0,"area":null},{"id":"2506.14854","title":"Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysis","created":"2025-06-17","cited_by_count":0,"area":null}],"next_cursor":null}