2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models
Shuo Yang, Changbai Li, Linlin Yang +6
Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redundancy or text-vision attention…
InfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection
Zichao Feng, Haodong Zhu, Jingying Yang +8
Robust object detection under adverse visual conditions remains a long-standing challenge for multi-modal perception systems. Existing fusion-based methods typically require both R…
Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalization
Haoran Pei, Yuguang Yang, Kexin Liu +2
Out-of-Distribution (OOD) generalization has become a primary metric for evaluating image analysis systems. Since deep learning models tend to capture domain-specific context, they…
Causally Guided Gaussian Perturbations for Out-Of-Distribution Generalization in Medical Imaging
Haoran Pei, Yuguang Yang, Kexin Liu +1
Out-of-distribution (OOD) generalization remains a central challenge in deploying deep learning models to real-world scenarios, particularly in domains such as biomedical images, w…
WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection
Haodong Zhu, Wenhao Dong, Linlin Yang +10
Leveraging the complementary characteristics of visible (RGB) and infrared (IR) imagery offers significant potential for improving object detection. In this paper, we propose WaveM…
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and Integration
Yuguang Yang, Runtang Guo, Sheng Wu +6
Interpreting complex deep networks, notably pre-trained vision-language models (VLMs), is a formidable challenge. Current Class Activation Map (CAM) methods highlight regions revea…