95 citations · 145 across the 6 of their papers we have counts for
6 papers
Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation
Tianle Chen, Zheda Mai, Ruiwen Li +1
Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation. Most existing methods rely on Clas…
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…
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness
Ruiwen Li, Zhibo Zhang, Jiani Li +5
Recent years have seen the introduction of a range of methods for post-hoc explainability of image classifier predictions. However, these post-hoc explanations may not always be fa…
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
Zheda Mai, Ruiwen Li, Hyunwoo Kim +1
Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data w…
Online Continual Learning in Image Classification: An Empirical Survey
Zheda Mai, Ruiwen Li, Jihwan Jeong +3
Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (…