Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing
College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）；Industrial Training Center, Guangdong Polytechnic Normal University（广东技术师范大学工业训练中心）
Although synthetic data can alleviate acquisition challenges in image dehazing tasks, it also introduces the problem of domain bias when dealing with small-scale data. This paper proposes a novel dual-branch collaborative unpaired dehazing model (DCM-dehaze) to address this issue. The proposed method consists of two collaborative branches: dehazing and contour constraints. Specifically, we design a dual depthwise separable convolutional module (DDSCM) to enhance the information expressiveness of deeper features and the correlation to shallow features. In addition, we construct a bidirectional contour function to optimize the edge features of the image to enhance the clarity and fidelity of the image details. Furthermore, we present feature enhancers via a residual dense architecture to eliminate redundant features of the dehazing process and further alleviate the domain deviation problem. Extensive experiments on benchmark datasets show that our method reaches the state-of-the-art. This project code will be available at \url{https://github.com/Fan-pixel/DCM-dehaze.
University of Waterloo（滑铁卢大学）；Beijing Institute of Technology（北京理工大学）；Xidian University（西安电子科技大学）；Cardiff University（卡迪夫大学）
Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introdu…
Xi'an Jiaotong University（西安交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs. Therefore image dehazing for perception enhancement and haze image synthesis for testing perception abilities are equivalently important in the development of such autonomous driving systems. From the view of image translation, these two problems are essentially dual with each other, which have the potentiality to be solved jointly. In this paper, we propose an unsupervised Image-to-Image Translation framework based on Variatio…
City University of Hong Kong（香港城市大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessment. As a result, one often resorts to subjective quality assessment, the most straightforward and reliable means of evaluating image enhancement. Conventional subjective testing requires manually pre-selecting a small set of visual examples, which may suffer from three sources of biases: 1) sampling bias due to the extremely sparse distribution of the selected samples in the image space; 2) algorithmic bias due to p…
Infrared-Assisted Single-Stage Framework for Joint Restoration and Fusion of Visible and Infrared Images under Hazy Conditions
Faculty of Information Engineering and Automation（信息工程与自动化学院）；Key Laboratory of Artificial Intelligence in Yunnan Province（云南省人工智能重点实验室）；School of Instrument Science and Opto-Electronic Engineering（仪器科学与光电工程学院）
Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overgeneralization, this study proposes to collaboratively optimize normal and abnormal feature distributions with the assistance of synthetic anomalies, namely collaborative discrepancy optimization (CDO). CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i.e., the margin and the o…
School of Electronic Information and Electrical Engineering（电子信息与电气工程学院）；Shanghai Jiao Tong University（上海交通大学）
Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation. A potential limitation of these clustering-based methods is that they always tend to introduce noisy labels, which will undoubtedly hamper the performance of our re-ID system. To handle this limitation, an intuitive solution is to utilize collaborative training to purify the pseudo label quality. However, there exists a challenge that the complementarity of two networks, which inevitably share a high similarity, becomes weak…
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available thematic products are often used for annotating RS images with zero-labeling-cost. However, such an approach may result in constructing a training set with noisy multi-labels, distorting the learning process. To address this problem, we propose a Consensual Collaborative Multi-Label Learning (CCML) method. The proposed CCML identifies, ranks and corrects…
School of Data and Computer Science, Sun Yat-sen University（中山大学数据与计算机学院）
Object detectors are usually trained with large amount of labeled data, which is expensive and labor-intensive. Pre-trained detectors applied to unlabeled dataset always suffer from the difference of dataset distribution, also called domain shift. Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier~(RPC) in the endemic two-stage detectors (e.g., Faster RCNN) demonstrate significantly different transferabil…
University of Science and Technology of China（中国科学技术大学）
With the rapid increase in online photo sharing activities, image obfuscation algorithms become particularly important for protecting the sensitive information in the shared photos. However, existing image obfuscation methods based on hand-crafted principles are challenged by the dramatic development of deep learning techniques. To address this problem, we propose to maximize the distribution discrepancy between the original image domain and the encrypted image domain. Accordingly, we introduce a collaborative training scheme: a discriminator $D$ is trained to discriminate the reconstructed im…
DAC-Pose: Dual-Agent Collaborative Framework for Pose-Guided Human Generation
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Shenzhen University of Advanced Technology（深圳理工大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
CoDS: Enhancing Collaborative Perception in Heterogeneous Scenarios via Domain Separation
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education and School of Computer Science and Technology, Beijing Jiaotong University（大数据与人工智能交通实验室（北京交通大学）及计算机科学与技术学院，北京交通大学）
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) have shown strong performance gains in RS. However, they usually require a high number of reliable training images annotated with multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost. However, m…
TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection
School of Biological Science and Medical Engineering, Southeast University（东南大学生物科学与医学工程学院）；Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University（东南大学江苏省生物材料与器件重点实验室）；TUGE Healthcare（图格医疗）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）；Laboratory of Image Science and Technology, Southeast University（东南大学图像科学与技术实验室）；Key Laboratory of Computer Network and Information Integration, Southeast University（东南大学计算机网络和信息集成重点实验室）；Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学计算机与数据科学系及生物医学工程系）
Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay
Institute of Software Chinese Academy of Science, Beijing, China（中国科学院软件研究所）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Advanced Institute of Big Data, Beijing, China（大数据先进研究院）
Learning from Heterogeneous Structural MRI via Collaborative Domain Adaptation for Late-Life Depression Assessment
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill（放射科与生物医学研究成像中心（BRIC），北卡罗来纳大学教堂山分校）；School of Computer Science and Engineering, Shandong University of Science and Technology（计算机科学与工程学院，山东科技大学）
EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception
Hunan University（湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing Images
School of Geosciences and Info-Physics, Central South University（中南大学地球科学与信息物理学院）；Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente（特文特大学地球信息科学与地球观测学院）
Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learning
State Key Laboratory of Virtual Reality Technology and Systems（虚拟现实技术与系统国家重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Beihang University（北航大学）
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）；Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University（暨南大学广东省中医信息技术重点实验室）；Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究学院（ICREA））
Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Graduate School of Engineering, Tohoku University（东北大学工学研究生院）；School of Biomedical Engineering, Anhui Medical University（安徽医科大学生物医学工程学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；The Hong Kong Polytechnic University（香港理工大学）
