6 papers
-CLIP: Text-Conditioned Contrastive Learning for Multi-Granular Vision-Language Alignment
Fatimah Zohra, Chen Zhao, Hani Itani +1
CLIP achieves strong zero-shot image-text retrieval by aligning global vision and text representations, yet it falls behind on fine-grained tasks even when fine-tuned on long, deta…
SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning
Fida Mohammad Thoker, Letian Jiang, Chen Zhao +4
Continued advances in self-supervised learning have led to significant progress in video representation learning, offering a scalable alternative to supervised approaches by removi…
SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning
Fida Mohammad Thoker, Letian Jiang, Chen Zhao +1
Masked video modeling, such as VideoMAE, is an effective paradigm for video self-supervised learning (SSL). However, they are primarily based on reconstructing pixel-level details…
BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding
Shuming Liu, Chen Zhao, Tianqi Xu +1
Large video-language models (VLMs) have demonstrated promising progress in various video understanding tasks. However, their effectiveness in long-form video analysis is constraine…
TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
Chen-Lin Zhang, Lin Sui, Shuming Liu +3
Temporal localization in untrimmed videos, which aims to identify specific timestamps, is crucial for video understanding but remains challenging. This task encompasses several sub…
OpenTAD: A Unified Framework and Comprehensive Study of Temporal Action Detection
Shuming Liu, Chen Zhao, Fatimah Zohra +10
Temporal action detection (TAD) is a fundamental video understanding task that aims to identify human actions and localize their temporal boundaries in videos. Although this field…