6 papers
VirtueBench: Evaluating Trustworthiness under Uncertainty in Long Video Understanding
Xueqing Yu, Bohan Li, Yan Li +1
Recent Vision-Language Models (VLMs) have made remarkable progress in multimodal understanding tasks, yet their evaluation on long video understanding remains unreliable. Due to li…
AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models
Xiaoquan Sun, Zetian Xu, Chen Cao +9
Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The execution of complex multi-step behaviors in VLA models can be impr…
UniAPO: Unified Multimodal Automated Prompt Optimization
Qipeng Zhu, Yanzhe Chen, Huasong Zhong +5
Prompting is fundamental to unlocking the full potential of large language models. To automate and enhance this process, automatic prompt optimization (APO) has been developed, dem…
UniCode: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation
Yanzhe Chen, Huasong Zhong, Yan Li +1
Unified multimodal large language models (MLLMs) have shown promise in jointly advancing multimodal understanding and generation, with visual codebooks discretizing images into tok…
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework
Xin Dong, Sen Jia, Ming Rui Wang +4
Recently, with the emergence of recent Multimodal Large Language Model (MLLM) technology, it has become possible to exploit its video understanding capability on different classifi…
Cream of the Crop: Harvesting Rich, Scalable and Transferable Multi-Modal Data for Instruction Fine-Tuning
Mengyao Lyu, Yan Li, Huasong Zhong +5
The hypothesis that pretrained large language models (LLMs) necessitate only minimal supervision during the fine-tuning (SFT) stage (Zhou et al., 2024) has been substantiated by re…