7 papers
Order-Aware Test-Time Adaptation: Leveraging Temporal Dynamics for Robust Streaming Inference
Young Kyung Kim, Oded Schlesinger, Qiangqiang Wu +2
Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams. However, existing methods typically treat these…
ViSS-R1: Self-Supervised Reinforcement Video Reasoning
Bo Fang, Yuxin Song, Qiangqiang Wu +3
Complex video reasoning remains a significant challenge for Multimodal Large Language Models (MLLMs), as current R1-based methodologies often prioritize text-centric reasoning deri…
Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking
Qiangqiang Wu, Yi Yu, Chenqi Kong +5
With the rise of social media, vast amounts of user-uploaded videos (e.g., YouTube) are utilized as training data for Visual Object Tracking (VOT). However, the VOT community has l…
Threading Keyframe with Narratives: MLLMs as Strong Long Video Comprehenders
Bo Fang, Wenhao Wu, Qiangqiang Wu +2
Employing Multimodal Large Language Models (MLLMs) for long video understanding remains a challenging problem due to the dilemma between the substantial number of video frames (i.e…
DistinctAD: Distinctive Audio Description Generation in Contexts
Bo Fang, Wenhao Wu, Qiangqiang Wu +2
Audio Descriptions (ADs) aim to provide a narration of a movie in text form, describing non-dialogue-related narratives, such as characters, actions, or scene establishment. Automa…
Learning Tracking Representations from Single Point Annotations
Qiangqiang Wu, Antoni B. Chan
Existing deep trackers are typically trained with largescale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate,…
Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM
Jia Wan, Qiangqiang Wu, Wei Lin +1
The existing crowd counting models require extensive training data, which is time-consuming to annotate. To tackle this issue, we propose a simple yet effective crowd counting meth…