most citedDescribe Anything in Medical Images

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models

Hao Zhang, Bo Huang, Zhenjia Li +6

Large Language Models (LLMs) have transformed both everyday life and scientific research. However, adapting LLMs from general-purpose models to specialized tasks remains challengin…

cs.CV2025

AdsQA: Towards Advertisement Video Understanding

Xinwei Long, Kai Tian, Peng Xu +10

Large language models (LLMs) have taken a great step towards AGI. Meanwhile, an increasing number of domain-specific problems such as math and programming boost these general-purpo…

cs.CE2025

RoadBench: A Vision-Language Foundation Model and Benchmark for Road Damage Understanding

Xi Xiao, Yunbei Zhang, Janet Wang +9

Accurate road damage detection is crucial for timely infrastructure maintenance and public safety, but existing vision-only datasets and models lack the rich contextual understandi…

cs.CV20252 cited

Describe Anything in Medical Images

Xi Xiao, Yunbei Zhang, Thanh-Huy Nguyen +10

Localized image captioning has made significant progress with models like the Describe Anything Model (DAM), which can generate detailed region-specific descriptions without explic…

cs.CV2025

CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization

Yingrui Ji, Xi Xiao, Gaofei Chen +5

Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success in cross-modal tasks such as zero-shot image classification and text-image retrieval by effectively al…