2 citations · 4 across the 5 of their papers we have counts for
5 papers
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding
Yandi Wang, Libin Zhan, Ziwei Huang +5
Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multimodal Large Language Models…
Towards Enhanced Image Generation Via Multi-modal Chain of Thought in Unified Generative Models
Yi Wang, Mushui Liu, Wanggui He +13
Unified generative models have shown remarkable performance in text and image generation. For image synthesis tasks, they adopt straightforward text-to-image (T2I) generation. Howe…
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts
Ziwei Huang, Wanggui He, Quanyu Long +8
Evaluating the quality of synthesized images remains a significant challenge in the development of text-to-image (T2I) generation. Most existing studies in this area primarily focu…
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
Wanggui He, Siming Fu, Mushui Liu +10
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis. In…
Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
Wenyi Xiao, Ziwei Huang, Leilei Gan +6
The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the ge…