5 papers · 1 filter
T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition
Di Yang, Mahmoud Ali, Quan Kong +2
Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing models rely primarily on appeara…
Synthetic Visual Genome 2: Extracting Large-scale Spatio-Temporal Scene Graphs from Videos
Ziqi Gao, Jieyu Zhang, Wisdom Oluchi Ikezogwo +10
We introduce Synthetic Visual Genome 2 (SVG2), a large-scale panoptic video scene graph dataset. SVG2 contains over 636K videos with 6.6M objects, 52.0M attributes, and 6.7M relati…
TrajTok: Learning Trajectory Tokens enables better Video Understanding
Chenhao Zheng, Jieyu Zhang, Jianing Zhang +6
Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video efficiency and scalability. While…
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Giacomo D'Amicantonio, Snehashis Majhi, Quan Kong +4
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD)…
Just Dance with ! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-worl…