50 citations · 317 across the 30 of their papers we have counts for
16 papers · 1 filter
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction
Jing Zhang, Jianwen Xie, Nick Barnes +1
Vision transformer networks have shown superiority in many computer vision tasks. In this paper, we take a step further by proposing a novel generative vision transformer with late…
Semi-supervised Salient Object Detection with Effective Confidence Estimation
Jiawei Liu, Jing Zhang, Nick Barnes
The success of existing salient object detection models relies on a large pixel-wise labeled training dataset, which is time-consuming and expensive to obtain. We study semi-superv…
GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation
Weixuan Sun, Jing Zhang, Zheyuan Liu +2
Weakly Supervised Semantic Segmentation (WSSS) is challenging, particularly when image-level labels are used to supervise pixel level prediction. To bridge their gap, a Class Activ…
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
Jing Zhang, Yuchao Dai, Mehrtash Harandi +3
Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty. In current aleatoric u…
A General Divergence Modeling Strategy for Salient Object Detection
Xinyu Tian, Jing Zhang, Yuchao Dai
Salient object detection is subjective in nature, which implies that multiple estimations should be related to the same input image. Most existing salient object detection models a…
Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation
Weixuan Sun, Jing Zhang, Nick Barnes
Image-level weakly supervised semantic segmentation (WSSS) relies on class activation maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative regio…