4 citations · 11 across the 15 of their papers we have counts for
7 papers · 1 filter
Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition
Zhisheng Zhong, Chengyao Wang, Yuqi Liu +12
As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI. However, prev…
DreamOmni: Unified Image Generation and Editing
Bin Xia, Yuechen Zhang, Jingyao Li +5
Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster…
VisionZip: Longer is Better but Not Necessary in Vision Language Models
Senqiao Yang, Yukang Chen, Zhuotao Tian +4
Recent advancements in vision-language models have enhanced performance by increasing the length of visual tokens, making them much longer than text tokens and significantly raisin…
RoboCoder: Robotic Learning from Basic Skills to General Tasks with Large Language Models
Jingyao Li, Pengguang Chen, Sitong Wu +3
The emergence of Large Language Models (LLMs) has improved the prospects for robotic tasks. However, existing benchmarks are still limited to single tasks with limited generalizati…
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
Zhongshen Zeng, Yinhong Liu, Yingjia Wan +16
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. Howeve…
VLPose: Bridging the Domain Gap in Pose Estimation with Language-Vision Tuning
Jingyao Li, Pengguang Chen, Xuan Ju +2
Thanks to advances in deep learning techniques, Human Pose Estimation (HPE) has achieved significant progress in natural scenarios. However, these models perform poorly in artifici…
MOODv2: Masked Image Modeling for Out-of-Distribution Detection
Jingyao Li, Pengguang Chen, Shaozuo Yu +2
The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples. While previous methods predomin…