7 citations · 17 across the 27 of their papers we have counts for
6 papers · 1 filter
SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation
Jingxuan Chen, Derek Yuen, Bin Xie +14
Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contender…
Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
Zaijing Li, Yuquan Xie, Rui Shao +3
Building a general-purpose agent is a long-standing vision in the field of artificial intelligence. Existing agents have made remarkable progress in many domains, yet they still st…
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…
RoboMP: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models
Qi Lv, Hao Li, Xiang Deng +3
Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or…
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought
Zaijing Li, Gongwei Chen, Rui Shao +3
Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks, thereby piquing the research community's curiosity for exploring their potentia…