3 papers
cs.CV2026
Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers
Edwin Kwadwo Tenagyei, Lei Wang, Ugochukwu Ejike Akpudo +2
Parameter-efficient fine-tuning (PEFT) has become a practical solution for adapting large pretrained vision transformers (ViTs) to downstream tasks while updating only a small subs…
cs.CV2025
Visual Explanation via Similar Feature Activation for Metric Learning
Yi Liao, Ugochukwu Ejike Akpudo, Jue Zhang +4
Visual explanation maps enhance the trustworthiness of decisions made by deep learning models and offer valuable guidance for developing new algorithms in image recognition tasks.…
cs.CV2025
TraNCE: Transformative Non-linear Concept Explainer for CNNs
Ugochukwu Ejike Akpudo, Yongsheng Gao, Jun Zhou +1
Convolutional neural networks (CNNs) have succeeded remarkably in various computer vision tasks. However, they are not intrinsically explainable. While the feature-level understand…