42 citations · 46 across the 9 of their papers we have counts for
10 papers
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-training
Zhenting Wang, Guofeng Cui, Yu-Jhe Li +2
Recent advances in reinforcement learning (RL)-based post-training have led to notable improvements in large language models (LLMs), particularly in enhancing their reasoning capab…
CalibQuant: 1-Bit KV Cache Quantization for Multimodal LLMs
Insu Han, Zeliang Zhang, Zhiyuan Wang +8
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance across diverse applications. However, their computational overhead during deployment remains a cri…
Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation
Yu-Jhe Li, Xinyang Zhang, Kun Wan +3
We tackle the challenge of open-vocabulary segmentation, where we need to identify objects from a wide range of categories in different environments, using text prompts as our inpu…
Treat Visual Tokens as Text? But Your MLLM Only Needs Fewer Efforts to See
Zeliang Zhang, Phu Pham, Wentian Zhao +6
By treating visual tokens from visual encoders as text tokens, Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse visual understanding tasks,…
Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach
Shijian Deng, Wentian Zhao, Yu-Jhe Li +4
Self-improvement in multimodal large language models (MLLMs) is crucial for enhancing their reliability and robustness. However, current methods often rely heavily on MLLMs themsel…
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
Lu Ling, Yichen Sheng, Zhi Tu +17
We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view sy…