322 citations · 835 across the 28 of their papers we have counts for
28 papers
When Depth Hurts: Reliability-Aware Geometry Distillation for Depth-Free RGB-D Salient Object Detection
Xuehao Wang, Jiaxin Hua, Runmei Li +4
Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable. Missing regions, blurred boundaries, and structural arti…
Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey
Zhenyu Wu, Fengmao Lv, Chenglizhao Chen +2
Colorectal polyp segmentation (CPS), an essential problem in medical image analysis, has garnered growing research attention. Recently, the deep learning-based model completely ove…
Rethinking Object Saliency Ranking: A Novel Whole-flow Processing Paradigm
Mengke Song, Linfeng Li, Dunquan Wu +2
Existing salient object detection methods are capable of predicting binary maps that highlight visually salient regions. However, these methods are limited in their ability to diff…
WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning
Guotao Wang, Chenglizhao Chen, Aimin Hao +2
To date, the widely adopted way to perform fixation collection in panoptic video is based on a head-mounted display (HMD), where users' fixations are collected while wearing an HMD…
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
Zhenyu Wu, Lin Wang, Wei Wang +4
Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achiev…
Synthetic Data Supervised Salient Object Detection
Zhenyu Wu, Lin Wang, Wei Wang +4
Although deep salient object detection (SOD) has achieved remarkable progress, deep SOD models are extremely data-hungry, requiring large-scale pixel-wise annotations to deliver su…
Salient Object Detection via Dynamic Scale Routing
Zhenyu Wu, Shuai Li, Chenglizhao Chen +2
Recent research advances in salient object detection (SOD) could largely be attributed to ever-stronger multi-scale feature representation empowered by the deep learning technologi…
Boundary-Guided Camouflaged Object Detection
Yujia Sun, Shuo Wang, Chenglizhao Chen +1
Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often…
A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key!
Chenglizhao Chen, Mengke Song, Wenfeng Song +2
Video saliency detection (VSD) aims at fast locating the most attractive objects/things/patterns in a given video clip. Existing VSD-related works have mainly relied on the visual…
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection
Chenglizhao Chen, Hengsen Wang, Yuming Fang +1
The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spat…
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Chong Peng, Yiqun Zhang, Yongyong Chen +3
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF,…
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
Chong Peng, Yang Liu, Yongyong Chen +5
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximatio…
Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception
Guotao Wang, Chenglizhao Chen, Deng-Ping Fan +2
Thanks to the rapid advances in deep learning techniques and the wide availability of large-scale training sets, the performance of video saliency detection models has been improvi…
Mutual Graph Learning for Camouflaged Object Detection
Qiang Zhai, Xin Li, Fan Yang +3
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between suc…
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
Chong Peng, Qian Zhang, Zhao Kang +2
Subspace clustering methods have been widely studied recently. When the inputs are 2-dimensional (2D) data, existing subspace clustering methods usually convert them into vectors,…
Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter
Zhenyu Wu, Shuai Li, Chenglizhao Chen +2
The real human attention is an interactive activity between our visual system and our brain, using both low-level visual stimulus and high-level semantic information. Previous imag…
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection
Xuehao Wang, Shuai Li, Chenglizhao Chen +3
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network arc…
Full Reference Screen Content Image Quality Assessment by Fusing Multi-level Structure Similarity
Chenglizhao Chen, Hongmeng Zhao, Huan Yang +2
The screen content images (SCIs) usually comprise various content types with sharp edges, in which the artifacts or distortions can be well sensed by the vanilla structure similari…
Depth Quality Aware Salient Object Detection
Chenglizhao Chen, Jipeng Wei, Chong Peng +1
The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D). The D quality usual…
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Zhenyu Wu, Shuai Li, Chenglizhao Chen +2
Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper an…
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Xuehao Wang, Shuai Li, Chenglizhao Chen +2
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is…
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
Chenglizhao Chen, Guotao Wang, Chong Peng +3
The current main stream methods formulate their video saliency mainly from two independent venues, i.e., the spatial and temporal branches. As a complementary component, the main t…
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
Chenglizhao Chen, Jia Song, Chong Peng +2
Previous video salient object detection (VSOD) approaches have mainly focused on designing fancy networks to achieve their performance improvements. However, with the slow-down in…
A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
Zhenyu Wu, Shuai Li, Chenglizhao Chen +2
Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy netw…