activity
20232025
most citedText-Region Matching for Multi-Label Image Recognition with Missing Labels

9 citations · 10 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2025

Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation

Huanli Zhuo, Leilei Ma, Haifeng Zhao +3

Although SAM-based single-source domain generalization models for medical image segmentation can mitigate the impact of domain shift on the model in cross-domain scenarios, these m…

cs.CV2025

Bridging Vision and Language: Optimal Transport-Driven Radiology Report Generation via LLMs

Haifeng Zhao, Yufei Zhang, Leilei Ma +2

Radiology report generation represents a significant application within medical AI, and has achieved impressive results. Concurrently, large language models (LLMs) have demonstrate…

cs.CV2025

Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning

LeiLei Ma, Shuo Xu, MingKun Xie +3

Modeling label correlations has always played a pivotal role in multi-label image classification (MLC), attracting significant attention from researchers. However, recent studies h…

cs.CV2025

Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning

Haifeng Zhao, Yuguang Jin, Leilei Ma

Significant advancements have been made in single label incremental learning (SLCIL),yet the more practical and challenging multi label class incremental learning (MLCIL) remains u…

cs.CV20249 cited

Text-Region Matching for Multi-Label Image Recognition with Missing Labels

Leilei Ma, Hongxing Xie, Lei Wang +3

Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering…

cs.CV20231 cited

SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification

Lei Wang, Yibing Zhan, Leilei Ma +3

Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for s…