3 papers
cs.CV2026
Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Tracking
Riku Inoue, Shogo Sato, Kazuhiko Murasaki +3
Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, making dataset construction ex…
cs.CV2026
Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking
Riku Inoue, Shogo Sato, Kazuhiko Murasaki +3
Multi-Object Tracking (MOT) in dynamic environments relies on robust temporal reasoning to maintain consistent object identities over time. Transformer-based end-to-end MOT models…
cs.CV2025
IPCD: Intrinsic Point-Cloud Decomposition
Shogo Sato, Takuhiro Kaneko, Shoichiro Takeda +5
Point clouds are widely used in various fields, including augmented reality (AR) and robotics, where relighting and texture editing are crucial for realistic visualization. Achievi…