95 citations · 148 across the 9 of their papers we have counts for
6 papers · 1 filter
TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation
Ruiwen Li, Zheda Mai, Chiheb Trabelsi +3
Weakly supervised semantic segmentation (WSSS) with only image-level supervision is a challenging task. Most existing methods exploit Class Activation Maps (CAM) to generate pixel-…
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification
Zhibo Zhang, Jongseong Jang, Chiheb Trabelsi +4
Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification. However, a key drawback of exis…
Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
Sam Sattarzadeh, Mahesh Sudhakar, Konstantinos N. Plataniotis +3
Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image…
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks
Mahesh Sudhakar, Sam Sattarzadeh, Konstantinos N. Plataniotis +3
Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models. Recently, perturba…
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
Sam Sattarzadeh, Mahesh Sudhakar, Anthony Lem +7
As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs).…
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation
Jihyeun Yoon, Kyungyul Kim, Jongseong Jang
Deep Neural Network based classifiers are known to be vulnerable to perturbations of inputs constructed by an adversarial attack to force misclassification. Most studies have focus…