3 papers
cs.CV2024
AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis
Townim F. Chowdhury, Vu Minh Hieu Phan, Kewen Liao +5
The integration of vision-language models such as CLIP and Concept Bottleneck Models (CBMs) offers a promising approach to explaining deep neural network (DNN) decisions using conc…
cs.CV2024
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Townim Faisal Chowdhury, Kewen Liao, Vu Minh Hieu Phan +7
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretab…
cs.CV2023
ChatGPT-guided Semantics for Zero-shot Learning
Fahimul Hoque Shubho, Townim Faisal Chowdhury, Ali Cheraghian +3
Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes…