1 citations · 2 across the 6 of their papers we have counts for
6 papers
MinCU: A Fine-Grained Benchmark for Grounded Minimal-Change Understanding in Image Pairs
Chaoqian Mu, Wenhao Wu, Zichen Liang +4
Localizing and describing fine-grained differences between near-identical images is a critical yet underexplored capability for multimodal large language models (MLLMs). Existing b…
FreeVA: Offline MLLM as Training-Free Video Assistant
Wenhao Wu
This paper undertakes an empirical study to revisit the latest advancements in Multimodal Large Language Models (MLLMs): Video Assistant. This study, namely FreeVA, aims to extend…
Automated Multi-level Preference for MLLMs
Mengxi Zhang, Wenhao Wu, Yu Lu +8
Current multimodal Large Language Models (MLLMs) suffer from ``hallucination'', occasionally generating responses that are not grounded in the input images. To tackle this challeng…
Dense Connector for MLLMs
Huanjin Yao, Wenhao Wu, Taojiannan Yang +7
Do we fully leverage the potential of visual encoder in Multimodal Large Language Models (MLLMs)? The recent outstanding performance of MLLMs in multimodal understanding has garner…
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
Yixuan Wu, Yizhou Wang, Shixiang Tang +5
We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini. Our a…
GPT4Ego: Unleashing the Potential of Pre-trained Models for Zero-Shot Egocentric Action Recognition
Guangzhao Dai, Xiangbo Shu, Wenhao Wu +2
Vision-Language Models (VLMs), pre-trained on large-scale datasets, have shown impressive performance in various visual recognition tasks. This advancement paves the way for notabl…