Research Background and Significance In recent years, weakly supervised online activity detection (WS-OAD), as an important topic in high-level video understanding, has garnered widespread attention. Its primary goal is to detect ongoing activities frame-by-frame in streaming videos using only inexpensive video-level annotations. This task holds si...

The Academic Report on Low-Frame-Rate Multi-Object Tracking Introduction and Research Background In recent years, multi-object tracking (MOT) has been widely applied in intelligent video surveillance, autonomous driving, and robotics vision. However, traditional MOT methods are predominantly designed for high-frame-rate videos and face significant ...

Few Annotated Pixels and Point Cloud Based Weakly Supervised Semantic Segmentation of Driving Scenes Background and Research Issues Semantic segmentation, a critical task in computer vision, has extensive applications in domains like autonomous driving. However, traditional fully-supervised semantic segmentation methods require exhaustive pixel-lev...

Rethinking Deep Learning Techniques for Error Correction in Biometric Data Background With the rapid development of information technology, biometric data has become increasingly important in identity verification and secure storage. Traditional cryptography relies on uniformly distributed and precisely reproducible random strings. However, most re...

Research on Adaptive Middle-Modality Alignment Learning for Visible-Infrared Cross-Modality Learning Background and Problem Statement Driven by the needs of intelligent surveillance systems, visible-infrared person re-identification (VIReID) has gradually become a prominent research topic. This task aims to achieve around-the-clock person recogniti...