3 citations · 8 across the 6 of their papers we have counts for
6 papers
Texture-guided Saliency Distilling for Unsupervised Salient Object Detection
Huajun Zhou, Bo Qiao, Lingxiao Yang +2
Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pr…
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds
Jiahao Zhu, Huajun Zhou, Zixuan Chen +2
3D deep models consuming point clouds have achieved sound application effects in computer vision. However, recent studies have shown they are vulnerable to 3D adversarial point clo…
Benchmarking Deep Models for Salient Object Detection
Huajun Zhou, Yang Lin, Lingxiao Yang +2
In recent years, deep network-based methods have continuously refreshed state-of-the-art performance on Salient Object Detection (SOD) task. However, the performance discrepancy ca…
Activation to Saliency: Forming High-Quality Labels for Completely Unsupervised Salient Object Detection
Huajun Zhou, Peijia Chen, Lingxiao Yang +2
Existing deep learning-based Unsupervised Salient Object Detection (USOD) methods rely on supervised pre-trained deep models. Moreover, they generate pseudo labels based on hand-cr…
Confidence-guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection
Peijia Chen, Jianhuang Lai, Guangcong Wang +1
Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, sp…
Contour Loss: Boundary-Aware Learning for Salient Object Segmentation
Zixuan Chen, Huajun Zhou, Xiaohua Xie +1
We present a learning model that makes full use of boundary information for salient object segmentation. Specifically, we come up with a novel loss function, i.e., Contour Loss, wh…