1 citations · 1 across the 2 of their papers we have counts for
10 papers
Speech-Omni-Lite: Portable Speech Interfaces for Vision-Language Models
Dehua Tao, Xuan Luo, Daxin Tan +5
While large-scale omni-models have demonstrated impressive capabilities across various modalities, their strong performance heavily relies on massive multimodal data and incurs sub…
ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving
Kai Chen, Ruiyuan Gao, Lanqing Hong +11
In this paper, we present details of the 1st W-CODA workshop, held in conjunction with the ECCV 2024. W-CODA aims to explore next-generation solutions for autonomous driving corner…
Reasoning-Aligned Perception Decoupling for Scalable Multi-modal Reasoning
Yunhao Gou, Kai Chen, Zhili Liu +5
Recent breakthroughs in reasoning language models have significantly advanced text-based reasoning. On the other hand, Multi-modal Large Language Models (MLLMs) still lag behind, h…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
Can Atomic Step Decomposition Enhance the Self-structured Reasoning of Multimodal Large Models?
Kun Xiang, Zhili Liu, Zihao Jiang +13
In this paper, we address the challenging task of multimodal mathematical reasoning by incorporating the ability of "slow thinking" into multimodal large language models (MLLMs). O…
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning
Yunhao Gou, Hansi Yang, Zhili Liu +8
Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), yet its effectiveness is often compromised by corrupted datasets with issues such as hallu…