7 citations · 18 across the 18 of their papers we have counts for
7 papers · 1 filter
HATS: Hardness-Aware Trajectory Synthesis for GUI Agents
Rui Shao, Ruize Gao, Bin Xie +5
Graphical user interface (GUI) agents powered by large vision-language models (VLMs) have shown remarkable potential in automating digital tasks, highlighting the need for high-qua…
HiconAgent: History Context-aware Policy Optimization for GUI Agents
Xurui Zhou, Gongwei Chen, Yuquan Xie +6
Graphical User Interface (GUI) agents require effective use of historical context to perform sequential navigation tasks. While incorporating past actions and observations can impr…
Beyond Quantity: Distribution-Aware Labeling for Visual Grounding
Yichi Zhang, Gongwei Chen, Jun Zhu +2
Visual grounding requires large and diverse region-text pairs. However, manual annotation is costly and fixed vocabularies restrict scalability and generalization. Existing pseudo-…
FALCON: Resolving Visual Redundancy and Fragmentation in High-resolution Multimodal Large Language Models via Visual Registers
Renshan Zhang, Rui Shao, Gongwei Chen +4
The incorporation of high-resolution visual input equips multimodal large language models (MLLMs) with enhanced visual perception capabilities for real-world tasks. However, most e…
Token-level Correlation-guided Compression for Efficient Multimodal Document Understanding
Renshan Zhang, Yibo Lyu, Rui Shao +3
Cropping high-resolution document images into multiple sub-images is the most widely used approach for current Multimodal Large Language Models (MLLMs) to do document understanding…
MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
Leyang Shen, Gongwei Chen, Rui Shao +2
Multimodal large language models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, a generalist MLLM typically underperforms compared…
LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge
Gongwei Chen, Leyang Shen, Rui Shao +2
Multimodal Large Language Models (MLLMs) have endowed LLMs with the ability to perceive and understand multi-modal signals. However, most of the existing MLLMs mainly adopt vision…