10 papers
M2P: Improving Visual Foundation Models with Mask-to-Point Weakly-Supervised Learning for Dense Point Tracking
Qiangqiang Wu, Tianyu Yang, Bo Fang +4
Tracking Any Point (TAP) has emerged as a fundamental tool for video understanding. Current approaches adapt Vision Foundation Models (VFMs) like DINOv2 via offline finetuning or t…
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…
Density-based Object Detection in Crowded Scenes
Chenyang Zhao, Jia Wan, Antoni B. Chan
Compared with the generic scenes, crowded scenes contain highly-overlapped instances, which result in: 1) more ambiguous anchors during training of object detectors, and 2) more pr…
Video Individual Counting for Moving Drones
Yaowu Fan, Jia Wan, Tao Han +2
Video Individual Counting (VIC) has received increasing attention for its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and…
Embodied Crowd Counting
Runling Long, Yunlong Wang, Jia Wan +5
Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectivenes…
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…