IET Computer Vision最新文献_第10页

Recognition of European mammals and birds in camera trap images using deep neural networks 利用深度神经网络识别相机捕获图像中的欧洲哺乳动物和鸟类

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-07-03 DOI: 10.1049/cvi2.12294

Daniel Schneider, Kim Lindner, Markus Vogelbacher, Hicham Bellafkir, Nina Farwig, Bernd Freisleben

{"title":"Recognition of European mammals and birds in camera trap images using deep neural networks","authors":"Daniel Schneider, Kim Lindner, Markus Vogelbacher, Hicham Bellafkir, Nina Farwig, Bernd Freisleben","doi":"10.1049/cvi2.12294","DOIUrl":"10.1049/cvi2.12294","url":null,"abstract":"Most machine learning methods for animal recognition in camera trap images are limited to mammal identification and group birds into a single class. Machine learning methods for visually discriminating birds, in turn, cannot discriminate between mammals and are not designed for camera trap images. The authors present deep neural network models to recognise both mammals and bird species in camera trap images. They train neural network models for species classification as well as for predicting the animal taxonomy, that is, genus, family, order, group, and class names. Different neural network architectures, including ResNet, EfficientNetV2, Vision Transformer, Swin Transformer, and ConvNeXt, are compared for these tasks. Furthermore, the authors investigate approaches to overcome various challenges associated with camera trap image analysis. The authors’ best species classification models achieve a mean average precision (mAP) of 97.91% on a validation data set and mAPs of 90.39% and 82.77% on test data sets recorded in forests in Germany and Poland, respectively. Their best taxonomic classification models reach a validation mAP of 97.18% and mAPs of 94.23% and 79.92% on the two test data sets, respectively.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 8","pages":"1162-1192"},"PeriodicalIF":1.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141683177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised multi-view clustering in computer vision: A survey 计算机视觉中的自监督多视角聚类：调查

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-07-02 DOI: 10.1049/cvi2.12299

Jiatai Wang, Zhiwei Xu, Xuewen Yang, Hailong Li, Bo Li, Xuying Meng

{"title":"Self-supervised multi-view clustering in computer vision: A survey","authors":"Jiatai Wang, Zhiwei Xu, Xuewen Yang, Hailong Li, Bo Li, Xuying Meng","doi":"10.1049/cvi2.12299","DOIUrl":"10.1049/cvi2.12299","url":null,"abstract":"In recent years, multi-view clustering (MVC) has had significant implications in the fields of cross-modal representation learning and data-driven decision-making. Its main objective is to cluster samples into distinct groups by leveraging consistency and complementary information among multiple views. However, the field of computer vision has witnessed the evolution of contrastive learning, and self-supervised learning has made substantial research progress. Consequently, self-supervised learning is progressively becoming dominant in MVC methods. It involves designing proxy tasks to extract supervisory information from image and video data, thereby guiding the clustering process. Despite the rapid development of self-supervised MVC, there is currently no comprehensive survey analysing and summarising the current state of research progress. Hence, the authors aim to explore the emergence of self-supervised MVC by discussing the reasons and advantages behind it. Additionally, the internal connections and classifications of common datasets, data issues, representation learning methods, and self-supervised learning methods are investigated. The authors not only introduce the mechanisms for each category of methods, but also provide illustrative examples of their applications. Finally, some open problems are identified for further investigation and development.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 6","pages":"709-734"},"PeriodicalIF":1.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fusing crops representation into snippet via mutual learning for weakly supervised surveillance anomaly detection 通过相互学习将作物表征融合到片段中，用于弱监督监控异常检测

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-07-02 DOI: 10.1049/cvi2.12289

Bohua Zhang, Jianru Xue

{"title":"Fusing crops representation into snippet via mutual learning for weakly supervised surveillance anomaly detection","authors":"Bohua Zhang, Jianru Xue","doi":"10.1049/cvi2.12289","DOIUrl":"10.1049/cvi2.12289","url":null,"abstract":"In recent years, the challenge of detecting anomalies in real-world surveillance videos using weakly supervised data has emerged. Traditional methods, utilising multi-instance learning (MIL) with video snippets, struggle with background noise and tend to overlook subtle anomalies. To tackle this, the authors propose a novel approach that crops snippets to create multiple instances with less noise, separately evaluates them and then fuses these evaluations for more precise anomaly detection. This method, however, leads to higher computational demands, especially during inference. Addressing this, our solution employs mutual learning to guide snippet feature training using these low-noise crops. The authors integrate multiple instance learning (MIL) for the primary task with snippets as inputs and multiple-multiple instance learning (MMIL) for an auxiliary task with crops during training. The authors’ approach ensures consistent multi-instance results in both tasks and incorporates a temporal activation mutual learning module (TAML) for aligning temporal anomaly activations between snippets and crops, improving the overall quality of snippet representations. Additionally, a snippet feature discrimination enhancement module (SFDE) refines the snippet features further. Tested across various datasets, the authors’ method shows remarkable performance, notably achieving a frame-level AUC of 85.78% on the UCF-Crime dataset, while reducing computational costs.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 8","pages":"1112-1126"},"PeriodicalIF":1.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FastFaceCLIP: A lightweight text-driven high-quality face image manipulation FastFaceCLIP：轻量级文本驱动的高质量人脸图像处理工具

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-07-02 DOI: 10.1049/cvi2.12295

Jiaqi Ren, Junping Qin, Qianli Ma, Yin Cao

{"title":"FastFaceCLIP: A lightweight text-driven high-quality face image manipulation","authors":"Jiaqi Ren, Junping Qin, Qianli Ma, Yin Cao","doi":"10.1049/cvi2.12295","DOIUrl":"10.1049/cvi2.12295","url":null,"abstract":"Although many new methods have emerged in text-driven images, the large computational power required for model training causes these methods to have a slow training process. Additionally, these methods consume a considerable amount of video random access memory (VRAM) resources during training. When generating high-resolution images, the VRAM resources are often insufficient, which results in the inability to generate high-resolution images. Nevertheless, recent Vision Transformers (ViTs) advancements have demonstrated their image classification and recognition capabilities. Unlike the traditional Convolutional Neural Networks based methods, ViTs have a Transformer-based architecture, leverage attention mechanisms to capture comprehensive global information, moreover enabling enhanced global understanding of images through inherent long-range dependencies, thus extracting more robust features and achieving comparable results with reduced computational load. The adaptability of ViTs to text-driven image manipulation was investigated. Specifically, existing image generation methods were refined and the FastFaceCLIP method was proposed by combining the image-text semantic alignment function of the pre-trained CLIP model with the high-resolution image generation function of the proposed FastFace. Additionally, the Multi-Axis Nested Transformer module was incorporated for advanced feature extraction from the latent space, generating higher-resolution images that are further enhanced using the Real-ESRGAN algorithm. Eventually, extensive face manipulation-related tests on the CelebA-HQ dataset challenge the proposed method and other related schemes, demonstrating that FastFaceCLIP effectively generates semantically accurate, visually realistic, and clear images using fewer parameters and less time.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 7","pages":"950-967"},"PeriodicalIF":1.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DualAD: Dual adversarial network for image anomaly detection⋆ DualAD：用于图像异常检测的双对抗网络

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-06-25 DOI: 10.1049/cvi2.12297

Yonghao Wan, Aimin Feng

{"title":"DualAD: Dual adversarial network for image anomaly detection⋆","authors":"Yonghao Wan, Aimin Feng","doi":"10.1049/cvi2.12297","DOIUrl":"10.1049/cvi2.12297","url":null,"abstract":"Anomaly Detection, also known as outlier detection, is critical in domains such as network security, intrusion detection, and fraud detection. One popular approach to anomaly detection is using autoencoders, which are trained to reconstruct input by minimising reconstruction error with the neural network. However, these methods usually suffer from the trade-off between normal reconstruction fidelity and abnormal reconstruction distinguishability, which damages the performance. The authors find that the above trade-off can be better mitigated by imposing constraints on the latent space of images. To this end, the authors propose a new Dual Adversarial Network (DualAD) that consists of a Feature Constraint (FC) module and a reconstruction module. The method incorporates the FC module during the reconstruction training process to impose constraints on the latent space of images, thereby yielding feature representations more conducive to anomaly detection. Additionally, the authors employ dual adversarial learning to model the distribution of normal data. On the one hand, adversarial learning was implemented during the reconstruction process to obtain higher-quality reconstruction samples, thereby preventing the effects of blurred image reconstructions on model performance. On the other hand, the authors utilise adversarial training of the FC module and the reconstruction module to achieve superior feature representation, making anomalies more distinguishable at the feature level. During the inference phase, the authors perform anomaly detection simultaneously in the pixel and latent spaces to identify abnormal patterns more comprehensively. Experiments on three data sets CIFAR10, MNIST, and FashionMNIST demonstrate the validity of the authors’ work. Results show that constraints on the latent space and adversarial learning can improve detection performance.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 8","pages":"1138-1148"},"PeriodicalIF":1.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAM-Y: Attention-enhanced hazardous vehicle object detection algorithm SAM-Y：注意力增强危险车辆目标检测算法

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-06-17 DOI: 10.1049/cvi2.12293

Shanshan Wang, Bushi Liu, Pengcheng Zhu, Xianchun Meng, Bolun Chen, Wei Shao, Liqing Chen

{"title":"SAM-Y: Attention-enhanced hazardous vehicle object detection algorithm","authors":"Shanshan Wang, Bushi Liu, Pengcheng Zhu, Xianchun Meng, Bolun Chen, Wei Shao, Liqing Chen","doi":"10.1049/cvi2.12293","DOIUrl":"10.1049/cvi2.12293","url":null,"abstract":"Vehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the detection of hazardous chemical vehicles, due to the complexity of the transportation environment, the small size and low resolution of the vehicle target etc., object detection becomes more difficult in the face of a complex background. In order to solve these problems, the authors propose an improved algorithm based on YOLOv5 to enhance the detection accuracy and efficiency of hazardous chemical vehicles. Firstly, in order to better capture the details and semantic information of hazardous chemical vehicles, the algorithm solves the problem of mismatch between the receptive field of the detector and the target object by introducing the receptive field expansion block into the backbone network, so as to improve the ability of the model to capture the detailed information of hazardous chemical vehicles. Secondly, in order to improve the ability of the model to express the characteristics of hazardous chemical vehicles, the authors introduce a separable attention mechanism in the multi-scale target detection stage, and enhances the prediction ability of the model by combining the object detection head and attention mechanism coherently in the feature layer of scale perception, the spatial location of spatial perception and the output channel of task perception. Experimental results show that the improved model significantly surpasses the baseline model in terms of accuracy and achieves more accurate object detection. At the same time, the model also has a certain improvement in inference speed and achieves faster inference ability.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 8","pages":"1149-1161"},"PeriodicalIF":1.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PSANet: Automatic colourisation using position-spatial attention for natural images PSANet：利用位置空间注意力为自然图像自动着色

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-06-16 DOI: 10.1049/cvi2.12291

Peng-Jie Zhu, Yuan-Yuan Pu, Qiuxia Yang, Siqi Li, Zheng-Peng Zhao, Hao Wu, Dan Xu

{"title":"PSANet: Automatic colourisation using position-spatial attention for natural images","authors":"Peng-Jie Zhu, Yuan-Yuan Pu, Qiuxia Yang, Siqi Li, Zheng-Peng Zhao, Hao Wu, Dan Xu","doi":"10.1049/cvi2.12291","DOIUrl":"10.1049/cvi2.12291","url":null,"abstract":"Due to the richness of natural image semantics, natural image colourisation is a challenging problem. Existing methods often suffer from semantic confusion due to insufficient semantic understanding, resulting in unreasonable colour assignments, especially at the edges of objects. This phenomenon is referred to as colour bleeding. The authors have found that using the self-attention mechanism benefits the model's understanding and recognition of object semantics. However, this leads to another problem in colourisation, namely dull colour. With this in mind, a Position-Spatial Attention Network(PSANet) is proposed to address the colour bleeding and the dull colour. Firstly, a novel new attention module called position-spatial attention module (PSAM) is introduced. Through the proposed PSAM module, the model enhances the semantic understanding of images while solving the dull colour problem caused by self-attention. Then, in order to further prevent colour bleeding on object boundaries, a gradient-aware loss is proposed. Lastly, the colour bleeding phenomenon is further improved by the combined effect of gradient-aware loss and edge-aware loss. Experimental results show that this method can reduce colour bleeding largely while maintaining good perceptual quality.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 7","pages":"922-934"},"PeriodicalIF":1.3,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge distillation of face recognition via attention cosine similarity review 通过注意力余弦相似性审查提炼人脸识别知识

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-05-31 DOI: 10.1049/cvi2.12288

Zhuo Wang, SuWen Zhao, WanYi Guo

引用次数: 0

SkatingVerse: A large-scale benchmark for comprehensive evaluation on human action understanding SkatingVerse：全面评估人类动作理解的大规模基准

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-05-30 DOI: 10.1049/cvi2.12287

Ziliang Gan, Lei Jin, Yi Cheng, Yu Cheng, Yinglei Teng, Zun Li, Yawen Li, Wenhan Yang, Zheng Zhu, Junliang Xing, Jian Zhao

{"title":"SkatingVerse: A large-scale benchmark for comprehensive evaluation on human action understanding","authors":"Ziliang Gan, Lei Jin, Yi Cheng, Yu Cheng, Yinglei Teng, Zun Li, Yawen Li, Wenhan Yang, Zheng Zhu, Junliang Xing, Jian Zhao","doi":"10.1049/cvi2.12287","DOIUrl":"10.1049/cvi2.12287","url":null,"abstract":"Human action understanding (HAU) is a broad topic that involves specific tasks, such as action localisation, recognition, and assessment. However, most popular HAU datasets are bound to one task based on particular actions. Combining different but relevant HAU tasks to establish a unified action understanding system is challenging due to the disparate actions across datasets. A large-scale and comprehensive benchmark, namely SkatingVerse is constructed for action recognition, segmentation, proposal, and assessment. SkatingVerse focus on fine-grained sport action, hence figure skating is chosen as the task object, which eliminates the biases of the object, scene, and space that exist in most previous datasets. In addition, skating actions have inherent complexity and similarity, which is an enormous challenge for current algorithms. A total of 1687 official figure skating competition videos was collected with a total of 184.4 h, exceeding four times over other datasets with a similar topic. SkatingVerse enables to formulate a unified task to output fine-grained human action classification and assessment results from a raw figure skating competition video. In addition, SkatingVerse can facilitate the study of HAU foundation model due to its large scale and abundant categories. Moreover, image modality is incorporated for human pose estimation task into SkatingVerse. Extensive experimental results show that (1) SkatingVerse significantly helps the training and evaluation of HAU methods, (2) the performance of existing HAU methods has much room to improve, and SkatingVerse helps to reduce such gaps, and (3) unifying relevant tasks in HAU through a uniform dataset can facilitate more practical applications. SkatingVerse will be publicly available to facilitate further studies on relevant problems.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 7","pages":"888-906"},"PeriodicalIF":1.3,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated finger vein presentation attack detection for various clients 针对各种客户端的联合手指静脉呈现攻击检测

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2024-05-30 DOI: 10.1049/cvi2.12292

Hengyu Mu, Jian Guo, Xingli Liu, Chong Han, Lijuan Sun

{"title":"Federated finger vein presentation attack detection for various clients","authors":"Hengyu Mu, Jian Guo, Xingli Liu, Chong Han, Lijuan Sun","doi":"10.1049/cvi2.12292","DOIUrl":"10.1049/cvi2.12292","url":null,"abstract":"Recently, the application of finger vein recognition has become popular. Studies have shown finger vein presentation attacks increasingly threaten these recognition devices. As a result, research on finger vein presentation attack detection (fvPAD) methods has received much attention. However, the current fvPAD methods have two limitations. (1) Most terminal devices cannot train fvPAD models independently due to a lack of data. (2) Several research institutes can train fvPAD models; however, these models perform poorly when applied to terminal devices due to inadequate generalisation. Consequently, it is difficult for threatened terminal devices to obtain an effective fvPAD model. To address this problem, the method of federated finger vein presentation attack detection for various clients is proposed, which is the first study that introduces federated learning (FL) to fvPAD. In the proposed method, the differences in data volume and computing power between clients are considered. Traditional FL clients are expanded into two categories: institutional and terminal clients. For institutional clients, an improved triplet training mode with FL is designed to enhance model generalisation. For terminal clients, their inability is solved to obtain effective fvPAD models. Finally, extensive experiments are conducted on three datasets, which demonstrate the superiority of our method.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 7","pages":"935-949"},"PeriodicalIF":1.3,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0