32 citations · 50 across the 6 of their papers we have counts for
6 papers
Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown
Zimeng Fang, Chao Liang, Xue Zhou +2
Multi-object tracking (MOT) emerges as a pivotal and highly promising branch in the field of computer vision. Classical closed-vocabulary MOT (CV-MOT) methods aim to track objects…
Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking
Yunfei Zhang, Chao Liang, Jin Gao +5
Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks by incorporating the extraction of appearance features as…
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition
Chao Liang, Zongxin Yang, Linchao Zhu +1
In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in…
One More Check: Making "Fake Background" Be Tracked Again
Chao Liang, Zhipeng Zhang, Xue Zhou +2
The one-shot multi-object tracking, which integrates object detection and ID embedding extraction into a unified network, has achieved groundbreaking results in recent years. Howev…
Rethinking the competition between detection and ReID in Multi-Object Tracking
Chao Liang, Zhipeng Zhang, Xue Zhou +3
Due to balanced accuracy and speed, one-shot models which jointly learn detection and identification embeddings, have drawn great attention in multi-object tracking (MOT). However,…
Feature Robust Optimal Transport for High-dimensional Data
Mathis Petrovich, Chao Liang, Ryoma Sato +6
Optimal transport is a machine learning problem with applications including distribution comparison, feature selection, and generative adversarial networks. In this paper, we propo…