5 papers
DenseMLLM: Standard Multimodal LLMs for Dense Prediction
Yi Li, Hongze Shen, Lexiang Tang +6
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding. However, extending these models to fine-grained dense predic…
MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
Tianqi Xiang, Yi Li, Qixiang Zhang +1
Recent advances in histopathology vision-language foundation models (VLFMs) have shown promise in addressing data scarcity for whole slide image (WSI) classification via zero-shot…
Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting
Zheang Huai, Hui Tang, Yi Li +2
Source-free domain adaptation (SFDA) for segmentation aims at adapting a model trained in the source domain to perform well in the target domain with only the source model and unla…
Token Activation Map to Visually Explain Multimodal LLMs
Yi Li, Hualiang Wang, Xinpeng Ding +2
Multimodal large language models (MLLMs) are broadly empowering various fields. Despite their advancements, the explainability of MLLMs remains less explored, hindering deeper unde…
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation
Yi Li, Haonan Wang, Qixiang Zhang +4
The emergence of unified multimodal understanding and generation models is rapidly attracting attention because of their ability to enhance instruction-following capabilities while…