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）
Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. Co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discission are conducted to provide a holistic overview of different saliency detection methods.
Despite significant progress, image saliency detection still remains a challenging task in complex scenes and environments. Integrating multiple different but complementary cues, like RGB and Thermal (RGB-T), may be an effective way for boosting saliency detection performance. The current research in this direction, however, is limited by the lack of a comprehensive benchmark. This work contributes such a RGB-T image dataset, which includes 821 spatially aligned RGB-T image pairs and their ground truth annotations for saliency detection purpose. The image pairs are with high diversity recorded…
National University of Sciences and Technology (NUST)（国立科技大学（NUST））
Understanding human visual attention and saliency is an integral part of vision research. In this context, there is an ever-present need for fresh and diverse benchmark datasets, particularly for insight into special use cases like crowded scenes. We contribute to this end by: (1) reviewing the dynamics behind saliency and crowds. (2) using eye tracking to create a dynamic human eye fixation dataset over a new set of crowd videos gathered from the Internet. The videos are annotated into three distinct density levels. (3) Finally, we evaluate state-of-the-art saliency models on our dataset to i…
Light field data exhibit favorable characteristics conducive to saliency detection. The success of learning-based light field saliency detection is heavily dependent on how a comprehensive dataset can be constructed for higher generalizability of models, how high dimensional light field data can be effectively exploited, and how a flexible model can be designed to achieve versatility for desktop computers and mobile devices. To answer these questions, first we introduce a large-scale dataset to enable versatile applications for RGB, RGB-D and light field saliency detection, containing 102 clas…
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 Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Spherical Vision Transformers for Audio-Visual Saliency Prediction in 360-Degree Videos
Department of Computer Science and Engineering, Koç University（计算机科学与工程系，科克大学）；Department of Psychology, Boğaziçi University（心理学系，博多伊大学）；National Institute of Advanced Industrial Science and Technology (AIST), Intelligent Platforms Research Institute（国家先进工业科学与技术研究院（AIST），智能平台研究机构）；Department of Psychology, Boğaziçi University University（心理学系，博多伊大学）；Department of Computer Engineering, Hacettepe University（计算机工程系，哈切塞特佩大学）；KUIS AI Center（KUIS人工智能中心）
The computation and memory needed for Convolutional Neural Network (CNN) inference can be reduced by pruning weights from the trained network. Pruning is guided by a pruning saliency, which heuristically approximates the change in the loss function associated with the removal of specific weights. Many pruning signals have been proposed, but the performance of each heuristic depends on the particular trained network. This leaves the data scientist with a difficult choice. When using any one saliency metric for the entire pruning process, we run the risk of the metric assumptions being invalidat…
Brand Visibility in Packaging: A Deep Learning Approach for Logo Detection, Saliency-Map Prediction, and Logo Placement Analysis
School of Electrical and Computer Engineering, University of Tehran, College of Engineering（德黑兰大学工程学院电气与计算机工程学院）；Faculty of Electrical Engineering, K. N. Toosi University of Technology（霍加托西理工大学电气工程学院）；Faculty of Engineering, Golestan University（戈尔甘大学工程学院）
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）
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…
Indraprastha Institute of Information Technology Delhi（印度德里英迪拉普拉萨塔信息技术学院）
Object co-segmentation has drawn significant attention in recent years, thanks to its clarity on the expected foreground, the shared object in a group of images. Saliency fusion has been one of the promising ways to carry it out. However, prior works either fuse saliency maps of the same image or saliency maps of different images to extract the expected foregrounds. Also, they rely on hand-crafted saliency extraction and correspondence processes in most cases. This paper revisits the problem and proposes fusing saliency maps of both the same image and different images. It also leverages advanc…
Isfahan University of Technology（伊斯法罕理工大学）；McMaster University（麦克马斯特大学）
Saliency detection is one of the most challenging problems in image analysis and computer vision. Many approaches propose different architectures based on the psychological and biological properties of the human visual attention system. However, there is still no abstract framework that summarizes the existing methods. In this paper, we offered a general framework for saliency models, which consists of five main steps: pre-processing, feature extraction, saliency map generation, saliency map combination, and post-processing. Also, we study different saliency models containing each level and co…
Visual saliency models have enjoyed a big leap in performance in recent years, thanks to advances in deep learning and large scale annotated data. Despite enormous effort and huge breakthroughs, however, models still fall short in reaching human-level accuracy. In this work, I explore the landscape of the field emphasizing on new deep saliency models, benchmarks, and datasets. A large number of image and video saliency models are reviewed and compared over two image benchmarks and two large scale video datasets. Further, I identify factors that contribute to the gap between models and humans a…
National Institute of Technology, Warangal（瓦朗加尔国家理工学院）；Aarhus University（奥胡斯大学）
Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting th…
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative convolutional neural network which predicts a saliency map for a frame, given the fixation map of the previous frame. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired and yet hard to display. This can be explained, on the one hand, by the dramatic improvement of 3D-capable acquisition equipment. On the other hand, despite the considerable progress…
School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）
Weakly-supervised object detection (WOD) is a challenging problems in computer vision. The key problem is to simultaneously infer the exact object locations in the training images and train the object detectors, given only the training images with weak image-level labels. Intuitively, by simulating the selective attention mechanism of human visual system, saliency detection technique can select attractive objects in scenes and thus is a potential way to provide useful priors for WOD. However, the way to adopt saliency detection in WOD is not trivial since the detected saliency region might be …
IIIT Sri City（斯里城印度信息技术学院）；CSIR-CEERI Pilani（科学与工业研究理事会-中央电子工程研究所皮拉尼）；CCS Computers Pvt Ltd（CCS计算机私人有限公司）；IIT Delhi（印度理工学院德里分校）；IIT Jodhpur（印度理工学院焦特布尔分校）
Synthesizing high quality saliency maps from noisy images is a challenging problem in computer vision and has many practical applications. Samples generated by existing techniques for saliency detection cannot handle the noise perturbations smoothly and fail to delineate the salient objects present in the given scene. In this paper, we present a novel end-to-end coupled Denoising based Saliency Prediction with Generative Adversarial Network (DSAL-GAN) framework to address the problem of salient object detection in noisy images. DSAL-GAN consists of two generative adversarial-networks (GAN) tra…
International Institute of Information Technology Hyderabad（海得拉巴国际信息技术研究所）
Learning computational models for visual attention (saliency estimation) is an effort to inch machines/robots closer to human visual cognitive abilities. Data-driven efforts have dominated the landscape since the introduction of deep neural network architectures. In deep learning research, the choices in architecture design are often empirical and frequently lead to more complex models than necessary. The complexity, in turn, hinders the application requirements. In this paper, we identify four key components of saliency models, i.e., input features, multi-level integration, readout architectu…
While datasets with single-label supervision have propelled rapid advances in image classification, additional annotations are necessary in order to quantitatively assess how models make predictions. To this end, for a subset of ImageNet samples, we collect segmentation masks for the entire object and $18$ informative attributes. We call this dataset RIVAL10 (RIch Visual Attributes with Localization), consisting of roughly $26k$ instances over $10$ classes. Using RIVAL10, we evaluate the sensitivity of a broad set of models to noise corruptions in foregrounds, backgrounds and attributes. In ou…
Northwestern Polytechnical University（西北工业大学）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）；Nanchang University（南昌大学）；Shanghai Jiao Tong University（上海交通大学）
Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain. By focusing on the benefits of joint auditory and visual information, most VSP methods are capable of exploiting semantic correlation between vision and audio modalities but ignoring the negative effects due to the temporal inconsistency of audio-visual intrinsics. Inspired by the biological inconsistency-correction within multi-sensory information, in this study, a consistency-aware audio-visual saliency prediction network (CASP-Net) is proposed, which takes a co…
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）
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 system but paid less attention to the audio aspect, while, actually, our audio system is the most vital complementary part to our visual system. Also, audio-visual saliency detection (AVSD), one of the most representative research topics for mimicking human perceptual mechanisms, is currently in its infancy, and none of the existing survey papers have touched on it, especially from the perspective of saliency detecti…
Dalian University of Technology（大连理工大学）；Pengcheng Lab（鹏城实验室）；Key Lab for Ubiquitous Network and Service Software of Liaoning Province（辽宁省泛在网络与服务软件重点实验室）
Weakly supervised salient object detection (WSOD) targets to train a CNNs-based saliency network using only low-cost annotations. Existing WSOD methods take various techniques to pursue single "high-quality" pseudo label from low-cost annotations and then develop their saliency networks. Though these methods have achieved good performance, the generated single label is inevitably affected by adopted refinement algorithms and shows prejudiced characteristics which further influence the saliency networks. In this work, we introduce a new multiple-pseudo-label framework to integrate more comprehe…
Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and ca…
S4Fusion: Saliency-aware Selective State Space Model for Infrared Visible Image Fusion
Sino-UK Joint Laboratory on Artificial Intelligence of Ministry of Science and Technology（科技部中英联合人工智能实验室）；International Joint Laboratory on Artificial Intelligence of Ministry of Education（教育部国际人工智能联合实验室）；School of Artificial Intelligence and Computer Science（人工智能与计算机科学学院）；College of Computer Science and Technology（计算机科学与技术学院）
MarkableAI Inc.（MarkableAI公司）；Aalto University（阿尔托大学）；Massachusetts Institute of Technology（麻省理工学院）
In this review, we examine the recent progress in saliency prediction and proposed several avenues for future research. In spite of tremendous efforts and huge progress, there is still room for improvement in terms finer-grained analysis of deep saliency models, evaluation measures, datasets, annotation methods, cognitive studies, and new applications. This chapter will appear in Encyclopedia of Computational Neuroscience.
Saliency methods generating visual explanatory maps representing the importance of image pixels for model classification is a popular technique for explaining neural network decisions. Hierarchical dynamic masks (HDM), a novel explanatory maps generation method, is proposed in this paper to enhance the granularity and comprehensiveness of saliency maps. First, we suggest the dynamic masks (DM), which enables multiple small-sized benchmark mask vectors to roughly learn the critical information in the image through an optimization method. Then the benchmark mask vectors guide the learning of lar…
Depth-induced Saliency Comparison Network for Diagnosis of Alzheimer's Disease via Jointly Analysis of Visual Stimuli and Eye Movements
Tianjin University（天津大学）；Zhejiang International Institute of Innovative Design（浙江国际创新设计研究院）；International Institute for Innovative Design and Intelligent Manufacturing of Tianjin University（天津大学创新设计与智能制造国际研究院）；China University of Geosciences（中国地质大学）；Lehigh University（利哈伊大学）；Tianjin Huanhu Hospital（天津环湖医院）；Tianjin Dementia Institute（天津痴呆症研究院）
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection. This detection technique, which had been devised for unrelated Computer Vision tasks, was empirically observed to play the role of an augmentation facilitator within the SSL protocol. This observation is rooted in our practical attempts to learn, by SSL-fashion, aerial imagery of solar panels, which exhibit challenging boundary patterns. Upon the successful integration of this techniq…
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, which leverages object contours to guide models to perceive salient object boundaries. Such a boundary-aware network can learn boundary-wise distinctions between salient objects and background, hence effectively facilitating the saliency detection. Yet the Contour Loss emphasizes on the local saliency. We further propose the hierarchical global attention module (HGAM), which forces the model hierarchically attend to gl…
This article presents an introduction to visual attention retargeting, its connection to visual saliency, the challenges associated with it, and ideas for how it can be approached. The difficulty of attention retargeting as a saliency inversion problem lies in the lack of one-to-one mapping between saliency and the image domain, in addition to the possible negative impact of saliency alterations on image aesthetics. A few approaches from recent literature to solve this challenging problem are reviewed, and several suggestions for future development are presented.
Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. Psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partiti…
Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（图像通信与网络工程研究所，上海交通大学）；Tianjin University（天津大学）；East China Normal University（华东师范大学）；Polytech Nantes, Université de Nantes（南特理工大学）
Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS（中国科学院计算技术研究所智能信息处理重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Kingsoft Cloud（金山云）；Noah’s Ark Lab, Huawei Technologies（华为技术有限公司诺亚方舟实验室）
To get more accurate saliency maps, recent methods mainly focus on aggregating multi-level features from fully convolutional network (FCN) and introducing edge information as auxiliary supervision. Though remarkable progress has been achieved, we observe that the closer the pixel is to the edge, the more difficult it is to be predicted, because edge pixels have a very imbalance distribution. To address this problem, we propose a label decoupling framework (LDF) which consists of a label decoupling (LD) procedure and a feature interaction network (FIN). LD explicitly decomposes the original sal…
This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM. Most SLAM methods treat all the features extracted from the images as equal importance during the optimization process. However, the salient feature points in scenes have more significant influence during the human navigation process. Therefore, we first propose a visual saliency model called SalVavNet in which we introduce a correlation module and propose an adapt…
Sichuan University（四川大学）；Wuhan University（武汉大学）；Nanjing University of Science and Technology（南京理工大学）；Nankai University（南开大学）
Salient object detection (SOD) is a long-standing research topic in computer vision and has drawn an increasing amount of research interest in the past decade. This paper provides the first comprehensive review and benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce preliminary knowledge on light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, one comparative study, and one brief review. Existing datasets for light field SOD ar…
The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach
IRT Saint-Exupéry（圣埃克苏佩里技术研究院）；IMT（法国国家矿业电信研究院）；INSA Toulouse（图卢兹国立应用科学学院）；ANITI Toulouse（图卢兹人工智能与数字技术研究所）；Université Paul Sabatier（保罗萨巴蒂尔大学）；ISAE-SUPAERO（法国高等航空航天学院）；Université de Toulouse（图卢兹大学）
IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；Department of Medical Equipment, the First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院医学装备部）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院深圳泛在数据赋能重点实验室）
Guangzhou University（广州大学）；Xidian University（西安电子科技大学）；City University of Hong Kong（香港城市大学）
Video quality is a primary concern for video service providers. In recent years, the techniques of video quality assessment (VQA) based on deep convolutional neural networks (CNNs) have been developed rapidly. Although existing works attempt to introduce the knowledge of the human visual system (HVS) into VQA, there still exhibit limitations that prevent the full exploitation of HVS, including an incomplete model by few characteristics and insufficient connections among these characteristics. To overcome these limitations, this paper revisits HVS with five representative characteristics, and f…
With the excellent accuracy and feasibility, the Neural Networks have been widely applied into the novel intelligent applications and systems. However, with the appearance of the Adversarial Attack, the NN based system performance becomes extremely vulnerable:the image classification results can be arbitrarily misled by the adversarial examples, which are crafted images with human unperceivable pixel-level perturbation. As this raised a significant system security issue, we implemented a series of investigations on the adversarial attack in this work: We first identify an image's pixel vulnera…
Dalian University of Technology（大连理工大学）；Tiwaki Co.Ltd（Tiwaki株式会社）
Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and …
