7 citations · 14 across the 12 of their papers we have counts for
12 papers
MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptation
Zhuonan Wang, Zhenxuan Fan, Siwen Tan +8
As industrial manufacturing scales, automating fine-grained product image analysis has become critical for quality control. However, existing approaches are hindered by limited dat…
Unified Personalized Understanding, Generating and Editing
Yu Zhong, Tianwei Lin, Ruike Zhu +9
Unified large multimodal models (LMMs) have achieved remarkable progress in general-purpose multimodal understanding and generation. However, they still operate under a ``one-size-…
Streaming Video Question-Answering with In-context Video KV-Cache Retrieval
Shangzhe Di, Zhelun Yu, Guanghao Zhang +7
We propose ReKV, a novel training-free approach that enables efficient streaming video question-answering (StreamingVQA), by seamlessly integrating with existing Video Large Langua…
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
Tianwei Lin, Wenqiao Zhang, Sijing Li +12
We present HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregres…
Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework
Jiang Liu, Bolin Li, Haoyuan Li +13
Efficient multimodal large language models (EMLLMs), in contrast to multimodal large language models (MLLMs), reduce model size and computational costs and are often deployed on re…
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…
AlignLLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation
Hongzhe Huang, Jiang Liu, Zhewen Yu +8
Recent advances in Multi-modal Large Language Models (MLLMs), such as LLaVA-series models, are driven by massive machine-generated instruction-following data tuning. Such automatic…
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
Tianwei Lin, Jiang Liu, Wenqiao Zhang +9
While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, especially…
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation
Fangxun Shu, Yue Liao, Le Zhuo +14
We introduce LLaVA-MoD, a novel framework designed to enable the efficient training of small-scale Multimodal Language Models (s-MLLM) by distilling knowledge from large-scale MLLM…
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…
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Wenqiao Zhang, Tianwei Lin, Jiang Liu +10
Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks. The prevailing MLLM paradigm,…