79 citations · 130 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…
Learning with Free Object Segments for Long-Tailed Instance Segmentation
Cheng Zhang, Tai-Yu Pan, Tianle Chen +3
One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects.…
Defending Adversarial Attacks via Semantic Feature Manipulation
Shuo Wang, Tianle Chen, Surya Nepal +3
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and…
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Shuo Wang, Tianle Chen, Shangyu Chen +3
Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as…
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
Shuo Wang, Surya Nepal, Carsten Rudolph +3
Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teac…
Generating Semantic Adversarial Examples via Feature Manipulation
Shuo Wang, Surya Nepal, Carsten Rudolph +3
The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wi…