6 papers
CFCML: A Coarse-to-Fine Crossmodal Learning Framework For Disease Diagnosis Using Multimodal Images and Tabular Data
Tianling Liu, Hongying Liu, Fanhua Shang +3
In clinical practice, crossmodal information including medical images and tabular data is essential for disease diagnosis. There exists a significant modality gap between these dat…
PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection
Xijun Lu, Hongying Liu, Fanhua Shang +2
Medical image anomaly detection faces unique challenges due to subtle, heterogeneous anomalies embedded in complex anatomical structures. Through systematic Grad-CAM analysis, we r…
LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression
Lianyu Hu, Fanhua Shang, Wei Feng +1
In this paper, we introduce LightVLM, a simple but effective method that can be seamlessly deployed upon existing Vision-Language Models (VLMs) to greatly accelerate the inference…
iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models
Lianyu Hu, Liqing Gao, Fanhua Shang +2
Recent methods have made notable progress in accelerating Large Vision-Language Models (LVLMs) by exploiting the inherent redundancy in visual inputs. Most existing approaches, how…
Deep Correlated Prompting for Visual Recognition with Missing Modalities
Lianyu Hu, Tongkai Shi, Wei Feng +2
Large-scale multimodal models have shown excellent performance over a series of tasks powered by the large corpus of paired multimodal training data. Generally, they are always ass…
Completed Feature Disentanglement Learning for Multimodal MRIs Analysis
Tianling Liu, Hongying Liu, Fanhua Shang +3
Multimodal MRIs play a crucial role in clinical diagnosis and treatment. Feature disentanglement (FD)-based methods, aiming at learning superior feature representations for multimo…