1 citations · 1 across the 12 of their papers we have counts for
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StreamFlow: Dynamic Memory Flows for Streaming Video Understanding
Muxin Fu, Yifan Zhang, Wentao Zhang +5
Streaming video understanding requires multimodal large language models (MLLMs) to preserve relevant evidence from continuously evolving streams under strict causality and bounded…
WorkDrive: Roadwork Chain of Causation for Autonomous Driving
Tianyi Jiang, Wen Zhang, Sihan Yang +2
Autonomous driving vision-language models (VLMs) struggle in roadwork zones, where familiar visual cues such as lane markings and permanent signs are altered or absent, and tempora…
SteerVTE: Seamless Video Text Editing with Style and Glyph Control
Kai Zeng, Moran Li, Zhengwei Wang +6
Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. Despite significant advances in the image domain,…
PEARL: Personalized Streaming Video Understanding Model
Yuanhong Zheng, Ruichuan An, Xiaopeng Lin +10
Human cognition of new concepts is inherently a streaming process: we continuously recognize new objects or identities and update our memories over time. However, current multimoda…
AD-MIR: Bridging the Gap from Perception to Persuasion in Advertising Video Understanding via Structured Reasoning
Binxiao Xu, Junyu Feng, Xiaopeng Lin +7
Multimodal understanding of advertising videos is essential for interpreting the intricate relationship between visual storytelling and abstract persuasion strategies. However, des…
VCU-Bridge: Hierarchical Visual Connotation Understanding via Semantic Bridging
Ming Zhong, Yuanlei Wang, Liuzhou Zhang +6
While Multimodal Large Language Models (MLLMs) excel on benchmarks, their processing paradigm differs from the human ability to integrate visual information. Unlike humans who natu…
Small-Large Collaboration: Training-efficient Concept Personalization for Large VLM using a Meta Personalized Small VLM
Sihan Yang, Huitong Ji, Shaolin Lu +6
Personalizing Vision-Language Models (VLMs) to transform them into daily assistants has emerged as a trending research direction. However, leading companies like OpenAI continue to…
Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos
Weifeng Lin, Xinyu Wei, Ruichuan An +7
We present Perceive Anything Model (PAM), a conceptually straightforward and efficient framework for comprehensive region-level visual understanding in images and videos. Our appro…
UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens
Ruichuan An, Sihan Yang, Renrui Zhang +10
Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for under…
Concept-as-Tree: A Controllable Synthetic Data Framework Makes Stronger Personalized VLMs
Ruichuan An, Kai Zeng, Ming Lu +5
Vision-Language Models (VLMs) have demonstrated exceptional performance in various multi-modal tasks. Recently, there has been an increasing interest in improving the personalizati…