1 citations · 2 across the 6 of their papers we have counts for
9 papers
X-SAM: From Segment Anything to Any Segmentation
Hao Wang, Limeng Qiao, Zequn Jie +6
Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding. Although…
Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation
Jinlong Li, Dong Zhao, Qi Zang +3
Continual Test Time Adaptation (CTTA) is a task that requires a source pre-trained model to continually adapt to new scenarios with changing target distributions. Existing CTTA met…
UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding
Yang Jiao, Haibo Qiu, Zequn Jie +4
We introduce UniToken, an auto-regressive generation model that encodes visual inputs through a combination of discrete and continuous representations, enabling seamless integratio…
RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving
Zhijian Huang, Chengjian Feng, Feng Yan +5
Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models. Despit…
TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
Shimin Chen, Xiaohan Lan, Yitian Yuan +2
Rapid development of large language models (LLMs) has significantly advanced multimodal large language models (LMMs), particularly in vision-language tasks. However, existing video…
VidCompress: Memory-Enhanced Temporal Compression for Video Understanding in Large Language Models
Xiaohan Lan, Yitian Yuan, Zequn Jie +1
Video-based multimodal large language models (Video-LLMs) possess significant potential for video understanding tasks. However, most Video-LLMs treat videos as a sequential set of…