6 papers
Boosting Robustness for All-Weather Self-Supervised Depth Estimation in Autonomous Driving
Mengshi Qi, Xiaoyang Bi, Xianlin Zhang +1
Self-supervised depth estimation is challenging for safe autonomous driving under various adverse weather conditions due to sensor perception degradation. These challenges arise fr…
Leveraging Metric Depth for Relative Depth Prediction
Xiaoyang Bi, Shuaikun Liu, Zhaohong Liu +5
We present our solution to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge. Predicting the relative depth in football scenarios is challenging, especially with…
A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation
Siyuan Li, Xiaoyang Bi, Mengshi Qi
Traffic accident anticipation -- predicting the likelihood of an imminent collision at every frame of a dashcam video -- is safety-critical yet difficult to scale, because collecti…
SoccerNet 2025 Challenges Results
Silvio Giancola, Anthony Cioppa, Marc Gutiérrez-Pérez +115
The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understandi…
DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency
Mengshi Qi, Pengfei Zhu, Xiangtai Li +4
Given a single labeled example, in-context segmentation aims to segment corresponding objects. This setting, known as one-shot segmentation in few-shot learning, explores the segme…
Towards Robust Unsupervised Attention Prediction in Autonomous Driving
Mengshi Qi, Xiaoyang Bi, Pengfei Zhu +1
Robustly predicting attention regions of interest for self-driving systems is crucial for driving safety but presents significant challenges due to the labor-intensive nature of ob…