4 citations · 8 across the 16 of their papers we have counts for
8 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…
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…
QuickLLaMA: Query-aware Inference Acceleration for Large Language Models
Jingyao Li, Han Shi, Xin Jiang +3
The capacity of Large Language Models (LLMs) to comprehend and reason over long contexts is pivotal for advancements in diverse fields. Yet, they still stuggle with capturing long-…
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
Shaoteng Liu, Haoqi Yuan, Minda Hu +5
Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embod…
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…
LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model
Senqiao Yang, Tianyuan Qu, Xin Lai +4
While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish differen…
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…