Human-Object Interaction (HOI) detection aims to detect visual relations between human and objects in images. One significant problem of HOI detection is that non-interactive human-object pair can be easily mis-grouped and misclassified as an action, especially when humans are close and performing similar actions in the scene. To address the mis-grouping problem, we propose a spatial enhancement approach to enforce fine-level spatial constraints in two directions from human body parts to the object center, and from object parts to the human center. At inference, we propose a human-object regrouping approach by considering the object-exclusive property of an action, where the target object should not be shared by more than one human. By suppressing non-interactive pairs, our approach can decrease the false positives. Experiments on V-COCO and HICO-DET datasets demonstrate our approach is more robust compared to the existing methods under the presence of multiple humans and objects in the scene.
University of Leicester（莱斯特大学）；Ocean University of China（中国海洋大学）；Queen Mary University of London（伦敦玛丽女王大学）
Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration. However, most of the existing underwater image enhancement strategies tend to consider enhancement and detection as two independent modules with no interaction, and the practice of separate optimization does not always help the underwater object detection task. In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection per…
Low-light image enhancement plays very important roles in low-level vision field. Recent works have built a large variety of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinex-inspired Unrolling with Architecture Search (RUAS), to construct lightweight yet effective enhancement network for low-light images in real-world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic…
InterLight: Leveraging Intrinsic Illumination Priors for Low-Light Image Enhancement
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（武汉大学计算机学院多媒体软件国家工程研究中心）；Department of Computer Science, University of Macau（澳门大学计算机科学系）；Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence（马尔代夫穆罕默德·本·扎耶德人工智能大学计算机视觉系）；School of Information Engineering, Guangdong University of Technology（广东技术大学信息工程学院）
UIEDP:Underwater Image Enhancement with Diffusion Prior
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement
Dalian University of Technology（大连理工大学）；Xidian University（西安电子科技大学）；Fudan University（复旦大学）；Shanghai Institute of Meteorological Science（上海市气象科学研究所）；The Hong Kong Polytechnic University（香港理工大学）
Di$^2$CycleSB: Towards High-Quality Unsupervised Nighttime Visibility Enhancement via Schrödinger Bridge Transformer
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Department of Automation, Tsinghua University（清华大学自动化系）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）
Gaussian Light Field Splatting: A Physical Prior-Driven Vision Transformer for Unsupervised Low-Light Image Enhancement
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；Herbert Wertheim College of Engineering, University of Florida（佛罗里达大学赫伯特·韦特海姆工程学院）；Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；National Innovation Center of Intelligent and Connected Vehicles（国家智能网联汽车创新中心）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
School of Information and Artificial Intelligence, Yunnan University of Finance and Economics（云南财经大学信息与人工智能学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）
Optimizing 4D Lookup Table for Low-light Video Enhancement via Wavelet Priori
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）；Chinese University of Hong Kong（香港中文大学）；College of Computer Science, Chongqing University（重庆大学计算机学院）
Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Nanyang Technological University（南洋理工大学）；Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University（南京大学极端性能光电器件与系统教育部重点实验室）
Adapting Large VLMs with Iterative and Manual Instructions for Generative Low-light Enhancement
Dalian University of Technology（大连理工大学）；National University of Singapore（新加坡国立大学）；Hong Kong Polytechnic University（香港理工大学）；University of California, San Francisco（加州大学旧金山分校）；Nanjing University of Science and Technology（南京理工大学）
The Hong Kong Polytechnic University（香港理工大学）；Nanjing University of Science and Technology（南京理工大学）
We propose an effective Structural Prior guided Generative Adversarial Transformer (SPGAT) to solve low-light image enhancement. Our SPGAT mainly contains a generator with two discriminators and a structural prior estimator (SPE). The generator is based on a U-shaped Transformer which is used to explore non-local information for better clear image restoration. The SPE is used to explore useful structures from images to guide the generator for better structural detail estimation. To generate more realistic images, we develop a new structural prior guided adversarial learning method by building …
Deep learning-based methods for low-light image enhancement typically require enormous paired training data, which are impractical to capture in real-world scenarios. Recently, unsupervised approaches have been explored to eliminate the reliance on paired training data. However, they perform erratically in diverse real-world scenarios due to the absence of priors. To address this issue, we propose an unsupervised low-light image enhancement method based on an effective prior termed histogram equalization prior (HEP). Our work is inspired by the interesting observation that the feature maps of …
OSA: Echocardiography Video Segmentation via Orthogonalized State Update and Anatomical Prior-aware Feature Enhancement
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University（香港理工大学护理学院智能健康中心）
Compensating scarce measurements by inferring them from computational models is a way to address ill-posed inverse problems. We tackle Limited Angle Tomography by completing the set of acquisitions using a generative model and prior-knowledge about the scanned object. Using a Generative Adversarial Network as model and Computer-Assisted Design data as shape prior, we demonstrate a quantitative and qualitative advantage of our technique over other state-of-the-art methods. Inferring a substantial number of consecutive missing measurements, we offer an alternative to other image inpainting techn…
Most deep models for underwater image enhancement resort to training on synthetic datasets based on underwater image formation models. Although promising performances have been achieved, they are still limited by two problems: (1) existing underwater image synthesis models have an intrinsic limitation, in which the homogeneous ambient light is usually randomly generated and many important dependencies are ignored, and thus the synthesized training data cannot adequately express characteristics of real underwater environments; (2) most of deep models disregard lots of favorable underwater prior…
Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement
College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能与光学电子学院（iOPEN））
Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex
Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；ETcTI, University Politehnica Timisoara（蒂米șoara工业大学ETcTI）；West University of Timisoara（蒂米șoara西大学）
LaCoVL-FER: Landmark-Guided Contrastive Learning Network with Vision-Language Enhancement for Facial Expression Recognition
School of Airspace and Engineering, Shandong University（山东大学航空航天与工程学院）；School of Computer Science and Technology, Shandong University of Finance and Economics（山东财经大学计算机科学与技术学院）；School of Psychology and Neuroscience, University of Glasgow（格拉斯哥大学心理学与神经科学学院）；Faculty of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）
DeepSPG: Exploring Deep Semantic Prior Guidance for Low-light Image Enhancement with Multimodal Learning
School of Cyber Science and Technology, Hubei University（湖北大学计算机科学与技术学院）；Department of Electrical Automation Design, Beijing Shougang International Engineering Technology（北京首钢国际工程技术部）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学部）；School of Computer Science and Technology, Harbin University of Science and Technology（哈尔滨理工大学计算机科学与技术学院）
DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子信息学院）；School of Artificial Intelligence, XiDian University（西安电子科技大学人工智能学院）
Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement
School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；School of Artificial Intelligence, Nanjing Normal University（人工智能学院，南京师范大学）
Sagiri: Low Dynamic Range Image Enhancement with Generative Diffusion Prior
Shanghai AI Lab（上海人工智能实验室）；Hefei University of Technology（合肥工业大学）；Snap Inc.（森宝公司）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）
ULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging
Kwame Nkrumah University of Science and Technology（科拉努姆大学科学与技术学院）；University of Catania（卡塔尼亚大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Medical Artificial Intelligence Lab（医学人工智能实验室）
