检索结果-内蒙古大学图书馆

27th International Conference on pattern recognition, ICPR 2024

作者： Roy, Ayush Palaiahnakote, Shivakumara Pal, Umapada Chanda, Sukalpa Department of Electrical Engineering Jadavpur University Kolkata India School of Science Engineering and Environment University of Salford Salford United Kingdom Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Østfold University College Halden Norway

ISBN: (纸本)9783031781032

Early diagnosis of retinal diseases is crucial for preventing blindness. However, due to background variations and degradation in the images, retinal vessel segmentation has become challenging. As a result, accurate segmentation of the retinal vessels is essential for enhancing diagnosis to identify the disease. To achieve this, inspired by the special ability of the attention mechanism, which detects vital regions, and UNet, which segments the vital region, we propose a combination of a new attention mechanism and modified UNet for segmenting vessels in retina images. In the proposed segmentation model, the convolutional blocks have been modified to capture multiscale spatial information by varying the convolution dilation rates. Similarly, the Trainable tanh activation (T-Tanh) is adapted in a new way to identify changes in the flow of the feature gradients to differentiate between the retinal vessel pixels and the background. Furthermore, to make the segmentation robust, the Gated Edge Attention (GEA) network is proposed. The effectiveness of the segmentation is demonstrated by testing on two benchmark datasets, namely, STARE and CHASE. The results show that the performance of the proposed method is superior to the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Promoted Self-Adjusting Correlation Learning for Facial Action Unit Detection

引用

IEEE Transactions on Affective Computing 2025年第2期16卷 697-711页

作者： Liu, Xin Yuan, Kaishen Niu, Xuesong Shi, Jingang Yu, Zitong Yue, Huanjing Yang, Jingyu Tianjin University School of Electrical and Information Engineering Tianjin300072 China Lappeenranta-Lahti University of Technology Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta53850 Finland Beijing Institute for General Artificial Intelligence Beijing100080 China Xi’an Jiaotong University School of Software Engineering Xi’an710049 China Great Bay University Dongguan523000 China

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7% and 12.0% of the parameters and FLOPs of the best method, respectively. © 2010-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A New StyleGAN Latent Space Based Model for Image Style Transfer 27th

A New StyleGAN Latent Space Based Model for Image Style Tran...

引用

27th International Conference on pattern recognition, ICPR 2024

作者： Dey, Rakesh Palaiahnakote, Shivakumara Bhattacharya, Saumik Chanda, Sukalpa Pal, Umapada Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Baranagar India School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Electronics Communication IIT-Kharagpur Kharagpur India Østfold University College Halden Norway

ISBN: (纸本)9783031781940

Cross-domain image style transfer task is an attractive topic for several applications, such as image-to-image style transfer, text-to-image style transfer, artistic image generation, etc. In cross-domain image style transfer tasks (e.g., image-to-image style transfer, artistic image-to-image style transfer, text-to-image style transfer, etc.), training becomes cumbersome due to differences in data distribution across domains and complex model architectures. Unlike existing domain adaptation and domain-independent methods that focus on robust and sufficient feature extraction, this work focuses on disentangling the latent space through latent optimization. For this purpose here we propose a new idea of styled image generation from the latent space of StyleGAN which works well for image-to-image and text-to-image style transfer. We critically analyzed the low-dimensional latent structure and its effect on cross-domain image style transfer tasks and finally proposed a method along with a latent optimizing procedure to overcome the problem of style transfer. The experimental results on different standard datasets show that the proposed model is robust, effective, and generic compared to the state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video 1

引用

7th Asian Conference on pattern recognition, ACPR 2023

作者： Halder, Arnab Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Nanjing University Nanjing China University of Technology Sydney Sydney Australia

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries

Blind Image Inpainting via Omni-dimensional Gated Attention ...

引用

IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Shruti S. Phutke Ashutosh Kulkarni Santosh Kumar Vipparthi Subrahmanyam Murala Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Rupnagar Punjab

Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discriminating between corrupted and valid regions. There exist very few approaches for blind image inpainting which sometimes fail at producing plausible inpainted images. Since they follow a common practice of predicting the corrupted regions and then inpaint them. To skip the corrupted region prediction step and obtain better results, in this work, we propose a novel end-to-end architecture for blind image inpainting consisting of wavelet query multi-head attention transformer block and the omni-dimensional gated attention. The proposed wavelet query multi-head attention in the transformer block provides encoder features via processed wavelet coefficients as query to the multi-head attention. Further, the proposed omni-dimensional gated attention effectively provides all dimensional attentive features from the encoder to the respective decoder. Our proposed approach is compared numerically and visually with existing state-of-the-art methods for blind image inpainting on different standard datasets. The comparative and ablation studies prove the effectiveness of the proposed approach for blind image inpainting. The testing code is available at : https://***/shrutiphutke/Blind_Omni_Wav_Net

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Lightweight Attention-Based Model for Emotion recognition on Distorted Social Media Face Images 1

引用

7th Asian Conference on pattern recognition, ACPR 2023

作者： Roy, Ayush Shivakumara, Palaiahnakote Pal, Umapada Gornale, Shivanand S. Liu, Cheng-Lin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Department of Computer Science Rani Channamma University Belagavi India Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

The recognition of human emotions remains a challenging task for social media images. This is due to distortions created by different social media conflict with the minute changes in facial expression. This study presents a new model called the Global Spectral-Spatial Attention Network (GSSAN), which leverages both local and global information simultaneously. The proposed model comprises a shallow Convolutional Neural Network (CNN) with an MBResNext block, which integrates the features extracted from MobileNet, ResNet, and DenseNet for extracting local features. In addition, to strengthen the discriminating power of the features, GSSAN incorporates Fourier features, which provide essential cues for minute changes in the face images. To test the proposed model for emotion recognition using social media images, we conduct experiments on two widely-used datasets: FER-2013 and AffectNet. The same benchmark datasets are uploaded and downloaded to create a distorted social media image dataset to test the proposed model. Experiments on distorted social media images dataset show that the model surpasses the accuracy of SOTA models by 0.69% for FER-2013 and 0.51% for AffectNet social media datasets. The same inference can be drawn from the experiments on standard datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

DITS: A New Domain Independent Text Spotter 27th

DITS: A New Domain Independent Text Spotter

引用

27th International Conference on pattern recognition, ICPR 2024

作者： Purkayastha, Kunal Sarkar, Shashwat Shivakumara, Palaiahnakote Pal, Umapada Ghosal, Palash Wu, Xiao-Jun Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Baranagar India School of Science Engineering and Environment University of Salford Manchester United Kingdom Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India Jiangnan University Wuxi China

ISBN: (纸本)9783031784941

Text spotting in diverse domains, such as drone-captured images, underwater scenes, and natural scene images, presents unique challenges due to variations in image quality, contrast, text appearance, background complexity, and external factors like water surface reflections and weather conditions. While most existing approaches focus on text spotting in natural scene images, we propose a Domain-Independent Text Spotter (DITS) that effectively handles multiple domains. We innovatively combine the Real-ESRGAN, developed for regular image enhancement, with the DeepSolo, developed for scene text spotting, in an end-to-end fashion for text detection and spotting on images of different domains. The key idea behind our approach is that improving image quality and text-spotting accuracy are complementary goals. Real-ESRGAN enhances image quality, making the text more discernible, while DeepSolo, a state-of-the-art text spotting model, accurately localizes and recognizes text in the enhanced images. We validate the superiority of our proposed model by evaluating it on datasets from drone, underwater, and scene domains (ICDAR 2015, CTW1500, and Total-Text). Furthermore, we demonstrate the domain independence of our model through cross-domain validation, where we train on one domain and test on others. Our dataset and code will be publicly available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

Open-Set Plankton recognition

arXiv

引用

arXiv 2025年

作者： Kareinen, Joona Skyttä, Annaliina Eerola, Tuomas Kraft, Kaisa Lensu, Lasse Suikkanen, Sanna Lehtiniemi, Maiju Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Finnish Environment Institute Helsinki Finland

This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an important role in marine ecosystems as primary producers and as a base of food webs. Given their sensitivity to environmental changes, fluctuations in plankton populations offer valuable information about oceans’ health and climate change motivating their monitoring. Modern automatic plankton imaging devices enable the collection of large-scale plankton image datasets, facilitating species-level analysis. Plankton species recognition can be seen as an image classification task and is typically solved using deep learning-based image recognition models. However, data collection in real aquatic environments results in imaging devices capturing a variety of non-plankton particles and plankton species not present in the training set. This creates a challenging fine-grained OSR problem, characterized by subtle differences between taxonomically close plankton species. We address this challenge by conducting extensive experiments on three OSR approaches using both phyto- and zooplankton images analyzing also on the effect of the rejection thresholds for OSR. The results demonstrate that high OSR accuracy can be obtained promoting the use of these methods in operational plankton research. We have made the data publicly available to the research community. © 2025, CC BY.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes

Diving into the Depths of Spotting Text in Multi-Domain Nois...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Alloy Das Sanket Biswas Umapada Pal Josep Lladós Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science Department Computer Vision Center Universitat Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models have been released in our Github.

关键词： Training Codes Accuracy Superresolution Benchmark testing Transformers Data models

来源：评论

学校读者我要写书评

暂无评论

COCAS+: Large-Scale Clothes-Changing Person Re-Identification With Clothes Templates

引用

IEEE Transactions on Circuits and Systems for Video Technology 2023年第4期33卷 1839-1853页

作者： Li, Shihua Chen, Haobin Yu, Shijie He, Zhiqun Zhu, Feng Zhao, Rui Chen, Jie Qiao, Yu Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences School of Microelectronics Beijing100049 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Key Laboratory of Computer Vision and Pattern Recognition The SIAT-SenseTime Joint Laboratory Beijing100045 China SenseTime Research Shenzhen518048 China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai200240 China

Recent years person re-identification (ReID) has been developed rapidly due to its broad practical applications. Most existing benchmarks assume that the same person wears the same clothes across captured images, while, in real-world scenarios, person may change his/her clothes frequently. Thus the Clothes-Changing person ReID (CC-ReID) problem is introduced and several related benchmarks are established. CC-ReID is a very difficult task as the main visual characteristics of a human body, clothes, are different between query and gallery, and clothes-irrelevant features are relatively weak. To promote the research and applications of person ReID in clothes-changing scenarios, in this paper, we introduce a new task called Clothes Template based Clothes-Changing person ReID (CTCC-ReID), where the query image is enhanced by a clothes template which shares similar visual patterns with the clothes of the target person image in the gallery. So, ReID methods are encouraged to jointly consider the original query image and the given clothes template for retrieval in the proposed CTCC-ReID setting. To facilitate research works on CTCC-ReID, we construct a novel large-scale ReID dataset named ClOthes ChAnging person Set Plus (COCAS+), which contains both realistic and synthetic clothes-changing person images with manually collected clothes templates. Furthermore, we propose a novel Dual-Attention Biometric-Clothes Transfusion Network (DualBCT-Net) for CTCC-ReID, which can effectively learn to extract biometric features from the original query person image and clothes features from the given clothes template and then fuse them through a Dual-Attention Fusion Module. Extensive experimental results show that the proposed CTCC-ReID setting and COCAS+ dataset can help greatly push the performance of clothes-changing ReID toward practical applications, and synthetic data is impressively effective for CTCC-ReID. What's more, the proposed DualBCT-Net shows significant improvements over

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：