The recognition of pig behavior plays a crucial role in smart farming and welfare assurance for pigs. Currently, in the field of pig behavior recognition, the lack of publicly available behavioral datasets not only limits the development of innovative algorithms but also hampers model robustness and algorithm optimization.This paper proposes a dataset containing 13 pig behaviors that significantly impact welfare.Based on this dataset, this paper proposes a spatial-temporal perception and enhancement networks based on the attention mechanism to model the spatiotemporal features of pig behaviors and their associated interaction areas in video data. The network is composed of a spatiotemporal perception network and a spatiotemporal feature enhancement network. The spatiotemporal perception network is responsible for establishing connections between the pigs and the key regions of their behaviors in the video data. The spatiotemporal feature enhancement network further strengthens the important spatial features of individual pigs and captures the long-term dependencies of the spatiotemporal features of individual behaviors by remodeling these connections, thereby enhancing the model's perception of spatiotemporal changes in pig behaviors. Experimental results demonstrate that on the dataset established in this paper, our proposed model achieves a MAP score of 75.92%, which is an 8.17% improvement over the best-performing traditional model. This study not only improces the accuracy and generalizability of individual pig behavior recognition but also provides new technological tools for modern smart farming. The dataset and related code will be made publicly available alongside this paper.
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding
China Agricultural University（中国农业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；AniEye Technology（安尼眼科技）；State Key Laboratory of Animal Nutrition and Feeding（动物营养与饲料国家重点实验室）
Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques can be used as preprocessing steps to help improve the overall image quality and in turn improve the overall effectiveness of a CNN. Existing image enhancement methods, however, are designed to improve the perceptual quality of an image for a human observer. In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily…
Beijing National Research Center for Information Science and Technology（北京国家信息科学与技术研究中心）；Institute for Brain and Cognitive Sciences, Tsinghua University（清华大学脑与认知科学研究院）；Tsinghua University（清华大学）
Imaging and perception in photon-limited scenarios is necessary for various applications, e.g., night surveillance or photography, high-speed photography, and autonomous driving. In these cases, cameras suffer from low signal-to-noise ratio, which degrades the image quality severely and poses challenges for downstream high-level vision tasks like object detection and recognition. Data-driven methods have achieved enormous success in both image restoration and high-level vision tasks. However, the lack of high-quality benchmark dataset with task-specific accurate annotations for photon-limited …
MvKeTR: Chest CT Report Generation with Multi-View Perception and Knowledge Enhancement
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院磁共振成像科）；Department of Electronic Science, Xiamen University（厦门大学电子科学系）
Low-light image enhancement is a promising solution to tackle the problem of insufficient sensitivity of human vision system (HVS) to perceive information in low light environments. Previous Retinex-based works always accomplish enhancement task by estimating light intensity. Unfortunately, single light intensity modelling is hard to accurately simulate visual perception information, leading to the problems of imbalanced visual photosensitivity and weak adaptivity. To solve these problems, we explore the precise relationship between light source and visual perception and then propose the visua…
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception
School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
HRSeg: High-Resolution Visual Perception and Enhancement for Reasoning Segmentation
Xiamen University（厦门大学）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics（教育部交叉计算与经济学重点实验室）；Shanghai University of Finance and Economics（上海财经大学）
In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experime…
CADENet: Condition-Adaptive Asynchronous Dual-Stream Enhancement Network for Adverse Weather Perception in Autonomous Driving
Computer Science & Engineering Department, German University in Cairo (GUC), Egypt（德国开罗大学（GUC）计算机科学与工程系，埃及）；C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems, Cairo, Egypt（认知驾驶系统实验室（C-DRiVeS Lab），开罗，埃及）
Downstream Task Inspired Underwater Image Enhancement: A Perception-Aware Study from Dataset Construction to Network Design
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电子与计算机工程系）
University of Science and Technology of China（中国科学技术大学）；Shandong University（山东大学）；The University of Sydney（悉尼大学）
Contrast enhancement and noise removal are coupled problems for low-light image enhancement. The existing Retinex based methods do not take the coupling relation into consideration, resulting in under or over-smoothing of the enhanced images. To address this issue, this paper presents a novel progressive Retinex framework, in which illumination and noise of low-light image are perceived in a mutually reinforced manner, leading to noise reduction low-light enhancement results. Specifically, two fully pointwise convolutional neural networks are devised to model the statistical regularities of am…
Gwangju Institute of Science and Technology (GIST)（光州科学技术院）；Korea Culture Technology Institute (KCTI)（韩国文化技术研究院）
Visual perception in autonomous driving is a crucial part of a vehicle to navigate safely and sustainably in different traffic conditions. However, in bad weather such as heavy rain and haze, the performance of visual perception is greatly affected by several degrading effects. Recently, deep learning-based perception methods have addressed multiple degrading effects to reflect real-world bad weather cases but have shown limited success due to 1) high computational costs for deployment on mobile devices and 2) poor relevance between image enhancement and visual perception in terms of the model…
Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；University of New South Wales（新南威尔士大学）；School of Computer Science and Technology, Shandong Technology and Business University（山东工商学院计算机科学与技术学院）；School of Information and Communication Engineering, Dalian Minzu University（大连民族大学信息与通信工程学院）；School of Innovation Experiment, Dalian University of Technology（大连理工大学创新实验学院）
University of Florida（佛罗里达大学）；SenseBrain Technology（感脑科技）
While humans can effortlessly transform complex visual scenes into simple words and the other way around by leveraging their high-level understanding of the content, conventional or the more recent learned image compression codecs do not seem to utilize the semantic meanings of visual content to their full potential. Moreover, they focus mostly on rate-distortion and tend to underperform in perception quality especially in low bitrate regime, and often disregard the performance of downstream computer vision algorithms, which is a fast-growing consumer group of compressed images in addition to …
Ocean University of China（中国海洋大学）；Qingdao University（青岛大学）；China University of Geosciences（中国地质大学）
Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement net…
Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition
The Laboratory of Intelligent Collaborative Computing of UESTC（UESTC智能协同计算实验室）；Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province（四川省 ubiquitous intelligence and trusted services 重点实验室）；Faculty of Information Technology, Monash University（墨尔本大学信息技术学院）
Dual Enhancement on 3D Vision-Language Perception for Monocular 3D Visual Grounding
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；College of Information Science and Engineering, Hunan Normal University（信息科学与工程学院，湖南师范大学）；School of Informatics, University of Edinburgh（信息学院，爱丁堡大学）
University of Technology Sydney（悉尼科技大学）；Zhejiang University（浙江大学）
Recently, remarkable progress has been made in weakly supervised object localization (WSOL) to promote object localization maps. The common practice of evaluating these maps applies an indirect and coarse way, i.e., obtaining tight bounding boxes which can cover high-activation regions and calculating intersection-over-union (IoU) scores between the predicted and ground-truth boxes. This measurement can evaluate the ability of localization maps to some extent, but we argue that the maps should be measured directly and delicately, i.e., comparing the maps with the ground-truth object masks pixe…
The limited dynamic range of commercial compact camera sensors results in an inaccurate representation of scenes with varying illumination conditions, adversely affecting image quality and subsequently limiting the performance of underlying image processing algorithms. Current state-of-the-art (SoTA) convolutional neural networks (CNN) are developed as post-processing techniques to independently recover under-/over-exposed images. However, when applied to images containing real-world degradations such as glare, high-beam, color bleeding with varying noise intensity, these algorithms amplify th…
In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present Deep SESR, a residual-in-residual network-based generative model that can learn to restore perceptual image qualities at 2x, 3x, or 4x higher spatial resolution. We supervise its training by formulating a multi-modal objective function that addresses the chrominance-specific underwater color degradation, lack of image sharpness, and loss in high-level feature representation. It is also s…
FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement
Guangxi Key Laboratory of Machine Vision and Intelligent Control（广西机器视觉与智能控制重点实验室）；Wuzhou University（梧州大学）；Faculty of Innovation Engineering（创新工程学院）；Macau University of Science and Technology（澳门科技大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳妇幼保健医院）；Southern Medical University（南方医科大学）；College of Big Data and Software Engineering（大数据与软件工程学院）；College of Electronical and Information Engineering（电子与信息工程学院）
University of Arkansas（阿肯色大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Face identity masking algorithms developed in recent years aim to protect the privacy of people in video recordings. These algorithms are designed to interfere with identification, while preserving information about facial actions. An important challenge is to preserve subtle actions in the eye region, while obscuring the salient identity cues from the eyes. We evaluated the effectiveness of identity-masking algorithms based on Canny filters, applied with and without eye enhancement, for interfering with identification and preserving facial actions. In Experiments 1 and 2, we tested human part…
Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception
School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；College of Science, China Agricultural University（中国农业大学理学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Engineering, Westlake University（西湖大学工程学院）
AquaFeat+: an Underwater Vision Learning-based Enhancement Method for Object Detection, Classification, and Tracking
Centro de Ciências Computacionais (C3), Universidade Federal do Rio Grande (FURG), Brazil（巴西里约格兰德联邦大学计算科学研究中心（C3））；Instituto de Ciências Exatas e Tecnologia (ICET), Universidade Federal do Amazonas (UFAM), Brazil（巴西亚马逊联邦大学精确科学与技术研究所（ICET））
LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement
National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；Agency for Science, Technology and Research, Singapore（新加坡科技研究局）；Advanced Institute of Big Data, Beijing（北京大数据研究院）
Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation
Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University（厦门大学水声通信与海洋信息技术教育部重点实验室）；PAMI Research Group, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系PAMI研究组）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
Image enhancement using the visible (V) and near-infrared (NIR) usually enhances useful image details. The enhanced images are evaluated by observers perception, instead of quantitative feature evaluation. Thus, can we say that these enhanced images using NIR information has better features in comparison to the computed features in the Red, Green, and Blue color channels directly? In this work, we present a new method to enhance the visible images using NIR information via edge-preserving filters, and also investigate which method performs best from a image features standpoint. We then show th…
Underwater visual perception is essentially important for underwater exploration, archeology, ecosystem and so on. The low illumination, light reflections, scattering, absorption and suspended particles inevitably lead to the critically degraded underwater image quality, which causes great challenges on recognizing the objects from the underwater images. The existing underwater enhancement methods that aim to promote the underwater visibility, heavily suffer from the poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we intr…
University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；City University of New York / CSI（纽约市立大学/斯塔滕岛学院）
In this paper a quaternion approach of enhancement method is proposed in which color in the image is considered as a single entity. This new method is referred as the alpha-rooting method of color image enhancement by the two-dimensional quaternion discrete Fourier transform (2-D QDFT) followed by a spatial transformation. The results of the proposed color image enhancement method are compared with its counterpart channel-by-channel enhancement algorithm by the 2-D DFT. The image enhancements are quantified to the enhancement measure that is based on visual perception referred as the color enh…
University of Science and Technology of China（中国科学技术大学）
Automatic color enhancement is aimed to adaptively adjust photos to expected styles and tones. For current learned methods in this field, global harmonious perception and local details are hard to be well-considered in a single model simultaneously. To address this problem, we propose a coarse-to-fine framework with non-local attention for color enhancement in this paper. Within our framework, we propose to divide enhancement process into channel-wise enhancement and pixel-wise refinement performed by two cascaded Convolutional Neural Networks (CNNs). In channel-wise enhancement, our model pre…
Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement
College of Aerospace Science and Engineering, National University of Defense Technology（航天科学与工程学院，国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）；Beijing Institute of Tracking and Telecommunication Technology（北京跟踪与电信技术研究所）；National Key Laboratory of Space Integrated Information System（空间一体化信息系统国家重点实验室）
Low-light images challenge both human perceptions and computer vision algorithms. It is crucial to make algorithms robust to enlighten low-light images for computational photography and computer vision applications such as real-time detection and segmentation. This paper proposes a semantic-guided zero-shot low-light enhancement network (SGZ) which is trained in the absence of paired images, unpaired datasets, and segmentation annotation. Firstly, we design an enhancement factor extraction network using depthwise separable convolution for an efficient estimate of the pixel-wise light deficienc…
University of British Columbia（不列颠哥伦比亚大学）；Toyota Institute of Technology（丰田工业大学）；Air Force Research Laboratory（空军研究实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel unsupervised thermal-to-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure co…
A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss
Beijing University of Posts and Telecommunications（北京邮电大学）；China Electric Power Research Institute Company Limited（中国电力科学研究院有限公司）；Tianjin University（天津大学）；State Grid Shanxi Marketing Service Center（国网山西省电力公司营销服务中心）
Reversible Unfolding Network for Concealed Visual Perception with Generative Refinement
Department of Biomedical Engineering, Duke University（生物医学工程系，杜克大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Nankai International Advanced Research Institute (SHENZHEN FUTIAN), Nankai University（南开国际先进研究院（深圳福田），南开大学）；College of Computer Science, Nankai University（计算机科学学院，南开大学）
A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement
School of Information Engineering, Xi’an Eurasia University, Xi’an, 710065, Shaanxi, China（西安欧亚大学信息工程学院）；Department of Electrical Engineering, University of Engineering and Technology Lahore（拉合尔工程与技术大学电气工程系）；Department of Computer Science, University of Engineering and Technology Lahore（拉合尔工程与技术大学计算机科学系）
ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement
School of Computer Science and Technology, Zhejiang Gongshang University（计算机科学与技术学院，浙江工商大学）；Depto. de Ingeniería Electro-fotónica, Universidad de Guadalajara（电子光子工程系，瓜达拉哈拉大学）
Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Department of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；Noah’s Ark Lab（诺亚实验室）；Department of Computer Science, University of Hong Kong（香港大学计算机科学系）
OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 Mobility学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
University of Notre Dame（圣母大学）；Zoom Video Communications（Zoom视频通讯公司）；Yale University（耶鲁大学）；Adobe Research（Adobe研究院）
The subtleties of human perception, as measured by vision scientists through the use of psychophysics, are important clues to the internal workings of visual recognition. For instance, measured reaction time can indicate whether a visual stimulus is easy for a subject to recognize, or whether it is hard. In this paper, we consider how to incorporate psychophysical measurements of visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can enforce consistency with human behavior. As a case study to assess…
Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement
National University of Singapore（新加坡国立大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing（福建省海洋信息感知与智能处理重点实验室）
Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving
Cleveland State University（克利夫兰州立大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Centre for Frontier AI Research (CFAR), A*STAR（新加坡科技研究局前沿人工智能研究中心）；New York University（纽约大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
本文提出 LightDiff 多条件扩散框架，通过非配对动态退化、多模态适配器和感知奖励强化学习，增强夜间驾驶图像并提升 3D 检测性能。
