A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges
Northwestern Polytechnical University（西北工业大学）；Institute for Infocomm Research, Agency for Science, Technology and Research（资讯通信研究院，科技研究局）；University of Central Florida（中佛罗里达大学）；Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（西安光学精密机械研究所，中国科学院）
Dingwen Zhang, Huazhu Fu, Junwei Han, Ali Borji, Xuelong Li
Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this paper, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field, and give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.
Review of Visual Saliency Detection with Comprehensive Information
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Inception Institute of Artificial Intelligence（Inception人工智能研究所）；School of Computer and Control Engineering, Nankai University（南开大学计算机与控制工程学院）；School of Computer Science Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Computer and Control Engineering, University of Chinese Academy of Sciences（中国科学院大学计算机与控制工程学院）
A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key!
China University of Petroleum (East China)（中国石油大学（华东））；Shandong University of Finance and Economics（山东财经大学）；Beijing Information Science and Technology University（北京信息科技大学）
UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Adaptive 3D Gaussian Splatting Video Streaming: Visual Saliency-Aware Tiling and Meta-Learning-Based Bitrate Adaptation
School of Electrical Engineering and Automation, Hefei University of Technology（电子工程与自动化学院，合肥工业大学）；Engineering Technology Research Center of Industrial Automation of Anhui Province（安徽省工业自动化工程技术研究中心）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）；The University of Electro-Communications（电子通信大学）
