1 citations · 1 across the 6 of their papers we have counts for
6 papers
Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention
Shezheng Song, Shasha Li, Shan Zhao +6
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language understanding, yet how they internally integrate visual and textual information remain…
Graph Laplacian Transformer with Progressive Sampling for Prostate Cancer Grading
Masum Shah Junayed, John Derek Van Vessem, Qian Wan +2
Prostate cancer grading from whole-slide images (WSIs) remains a challenging task due to the large-scale nature of WSIs, the presence of heterogeneous tissue structures, and diffic…
RAG-Adapter: A Plug-and-Play RAG-enhanced Framework for Long Video Understanding
Xichen Tan, Yunfan Ye, Yuanjing Luo +3
Multi-modal Large Language Models (MLLMs) capable of video understanding are advancing rapidly. To effectively assess their video comprehension capabilities, long video understandi…
How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization
Shezheng Song, Hao Xu, Jun Ma +5
Large Language Models (LLMs) exhibit strong general language capabilities. However, fine-tuning these models on domain-specific tasks often leads to catastrophic forgetting, where…
MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image
Shezheng Song, Chengxiang He, Shan Zhao +4
Multimodal large language models (MLLMs) have shown remarkable progress in high-level semantic tasks such as visual question answering, image captioning, and emotion recognition. H…
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
Shezheng Song, Shasha Li, Shan Zhao +8
Multimodal aspect-based sentiment analysis (MABSA) aims to understand opinions in a granular manner, advancing human-computer interaction and other fields. Traditionally, MABSA met…