3 citations · 3 across the 6 of their papers we have counts for
7 papers
DialogGraph-LLM: Graph-Informed LLMs for End-to-End Audio Dialogue Intent Recognition
HongYu Liu, Junxin Li, Changxi Guo +5
Recognizing speaker intent in long audio dialogues among speakers has a wide range of applications, but is a non-trivial AI task due to complex inter-dependencies in speaker uttera…
UniGame: Turning a Unified Multimodal Model Into Its Own Adversary
Zhaolong Su, Wang Lu, Hao Chen +2
Unified Multimodal Models (UMMs) have shown impressive performance in both understanding and generation with a single architecture. However, UMMs still exhibit a fundamental incons…
MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
Jiale Li, Mingrui Wu, Zixiang Jin +5
Despite growing interest in hallucination in Multimodal Large Language Models, existing studies primarily focus on single-image settings, leaving hallucination in multi-image scena…
Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation
Hongxiang Zhang, Hao Chen, Muhao Chen +1
Recent decoding methods improve the factuality of large language models (LLMs) by refining how the next token is selected during generation. These methods typically operate at the…
Corruption-Aware Training of Latent Video Diffusion Models for Robust Text-to-Video Generation
Chika Maduabuchi, Hao Chen, Yujin Han +1
Latent Video Diffusion Models (LVDMs) have achieved state-of-the-art generative quality for image and video generation; however, they remain brittle under noisy conditioning, where…
On the robustness of multimodal language model towards distractions
Ming Liu, Hao Chen, Jindong Wang +1
Although vision-language models (VLMs) have achieved significant success in various applications such as visual question answering, their resilience to prompt variations remains an…
On Fairness of Unified Multimodal Large Language Model for Image Generation
Ming Liu, Hao Chen, Jindong Wang +3
Unified multimodal large language models (U-MLLMs) have demonstrated impressive performance in visual understanding and generation in an end-to-end pipeline. Compared with generati…