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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2177 条记录，以下是151-160 订阅

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DATR: Domain Agnostic Text Recognizer 27th

DATR: Domain Agnostic Text Recognizer

引用

27th International conference on pattern recognition, ICPR 2024

作者： Purkayastha, Kunal Sarkar, Shashwat Palaiahnakote, Shivakumara Pal, Umapada Ghosal, Palash Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata 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

ISBN: (纸本)9783031784460

Recognizing text extracted from multiple domains is complex and challenging because complexities vary from one domain to another. Most existing methods focus either on natural scene text or specific text type but not text of multiple domains, namely, scene, underwater, and drone texts. In addition, the state-of-the-art models ignore the vital cues that exist in multiple instances of the text. this paper presents a new method called the Student-Teacher-Assistant (STA) network, which involves dual CLIP models to exploit cues in multiple text instances. the model that uses ResNet50 in its image encoder is called helper CLIP, while the model that uses ViT in its image encoder is called primary CLIP. the proposed work processes both models simultaneously to extract visual and textual features through image and text encoders. Our work uses cosine similarity for the randomly chosen input image to detect instances similar to the input image. the input and similar instances are supplied to primary and helper CLIPs for visual and textual feature extraction. the outputs of dual CLIPs are fused in a different way through the alignment step for recognizing text accurately, irrespective of domains. To demonstrate the proposed model’s significance, experiments are conducted on a set of standard natural scene text datasets (regular and irregular), underwater images, and drone images. the results on three different domains show that the proposed model outperforms the state-of-the-art recognition models. the datasets and code for public use in training and testing shall be made available on Github. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Underwater imaging

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DereflectFormer: vision Transformers for Single Image Reflection Removal 27th

DereflectFormer: Vision Transformers for Single Image Reflec...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Wei, Ao Zhang, Hanbin Zhao, Erhu ShanghaiTech University Shanghai China Shanghai Innovation Center for Processor Technologies Shanghai China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031783043

In this paper, we address the challenge of single image reflection removal (SIRR), a crucial task in computer vision that involves eliminating undesirable reflections from images captured through glass surfaces. Current state-of-the-art methods typically rely on convolutional neural networks (CNNs) and often make certain assumptions about the appearance of reflections, which may not hold true in real-world scenarios. To overcome these limitations, we propose a novel Transformer-based approach, DereflectFormer, inspired by the Swin Transformer. Our architecture introduces a new module, the Depthwise Multi-Activation Feed-Forward Network (DMFN), which leverages depthwise convolution and a dual-stream ReLU-GELU activation function to enhance detail extraction capability. We also employ a synthetic dataset and a synthesis method for training, which allows our model to fully exploit the capabilities of Transformer architectures. Based on experimental results, we demonstrate that our approach performs better than state-of-the-art methods, providing more accurate and robust results in various real-world scenarios. Furthermore, our ablation studies reveal that each component of our architecture contributes significantly to its performance, offering valuable insights for future research in the field of single image reflection removal. the code and dataset is available at https://***/Agent76ow/DereflectFormer. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Automated Maternal Fetal Ultrasound Image Identification Using a Hybrid vision Transformer Model 27th

Automated Maternal Fetal Ultrasound Image Identification Usi...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Krishna, thunakala Bala Poreddy, Ajay Kumar Reddy Sindhu, Kolla Gnapika Kokil, Priyanka Lab Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai600127 India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

ISBN: (纸本)9783031781940

Ultrasound (US) technology has revolutionized prenatal care by offering noninvasive, real-time visualization of maternal-fetal anatomy. the accurate classification of maternal-fetal US planes is a critical segment of effective prenatal diagnosis. However, the inherent inter-class variance among different fetal US images presents a significant hurdle, making fetal anatomy detection a laborious and time-consuming task, even for experienced sonographers. this paper proposes a novel approach using a Hybrid vision Transformer (H-ViT) for automated fetal anatomical plane classification to address these challenges. the proposed method utilizes hierarchical features extracted from DenseNet-121, which are then inputted into the vision transformer to analyze complex spatial relationships and patterns within fetal US images. By incorporating both global and local features, the proposed method enhances feature discriminability, thus alleviating low inter-class variance. the effectiveness of the H-ViT is evaluated using the largest publicly available maternal-fetal US image dataset. the experimental results rigorously demonstrate the superiority of our approach, achieving an accuracy of 96.60% compared to other state-of-the-art techniques. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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GSMNet: Towards Long-Term Trajectory Prediction by Integrating Multi-scale Information 17th

GSMNet: Towards Long-Term Trajectory Prediction by Integrat...

引用

17th Asian conference on computer vision, ACCV 2024

作者： Liu, Shaohua Wang, Yisu Zhu, Yinglong Yao, Pengfei Mao, Tianlu Wang, Zhaoqi Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9789819609000

Predicting the future trajectories of pedestrians is a vital task for many applications, such as autonomous driving and robot navigation. Most existing methods only predict short-term trajectories. In this paper, we challenge the problem of long-term trajectory prediction. Different from short-term prediction which focus most on the local information, long-term prediction needs to model future trajectory with multi-scale information hierarchically from the multimodal global destination, to mid-distance scene layout limitation, other agent movement and finally the local history motion pattern. the destination reflects pedestrian long-term multimodal goal, the scene layout along with interaction constrains the possible path choice, and history motion pattern guides the future movement. We propose GSMNet, which achieves effective long-term trajectory prediction by integrating multi-scale factors: multimodal goals, scene interaction and motion patterns. We design separate modules to extract different scale features. Multi-layer-perceptron extracts the local-scale feature from history motion pattern. U-Net with attention captures the mid-scale pedestrian-scene correlation feature and goal feature with scene layout at global-scale. Finally, combining multi-scale feature to predict future trajectories. Experiments on SDD dataset and Eth-UCY dataset show that proposed GSMNet outperforms the previous state-of-the-art for both long-term and short-term trajectory prediction task. Qualitative results show GSMNet generates more reasonable trajectories. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Trajectories

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Illuminating the Dark: Unpaired Retinex and FFT-Based Low-Light Image Enhancement 27th

Illuminating the Dark: Unpaired Retinex and FFT-Based Low-...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Li, Zeyu Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781247

In the field of computer vision, enhancing low-light images is a significant challenge, primarily due to the reliance on high-quality paired low-light and high-light images in supervised learning methods, which are expensive to acquire. this paper presents an unpaired approach for low-light image enhancement, integrating a Joint Estimation Network and a Multi-Domain Feature Fusion Network. the Joint Estimation Network is trained exclusively with pairs of low-light images of the same scene, while the Multi-Domain Feature Fusion Network is trained solely with normal-light images that are not paired with the aforementioned low-light images. the Joint Estimation Network decomposes low-light images into components of illumination, reflectance, and noise. After enhancing the illumination, it passes these components, along with the reflectance, to the Multi-Domain Feature Fusion Network. the Multi-Domain Feature Fusion Network employs multi-scale encoder-decoder modules and frequency domain adjustments to enhance details and maintain global consistency. Our method addresses the issues of insufficient illumination and high noise in low-light images, improving visual quality without the need for paired images, thereby increasing the model’s practicality in real-world applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supervised learning

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YCB-Ev: Event-vision Dataset for 6 DoF Object Pose Estimation 4th

YCB-Ev: Event-Vision Dataset for 6 DoF Object Pose Estimati...

引用

4th International conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Rojtberg, Pavel Pöllabauer, thomas Fraunhofer IGD Darmstadt Germany

ISBN: (纸本)9789819787043

Our work introduces the YCB-Ev dataset, which contains synchronized RGB-D frames and event data that enables evaluating 6DoF object pose estimation algorithms using these modalities. this dataset provides ground truth 6DoF object poses for the same 21 YCB objects [1] that were used in the YCB-Video (YCB-V) dataset [19], enabling the evaluation of algorithm performance when transferred across datasets. the dataset consists of 21 synchronized event and RGB-D sequences, amounting to a total of 7:43 min of video. Notably, 12 of these sequences feature the same object arrangement as the YCB-V subset used in the BOP challenge [16]. Our dataset is the first to provide ground truth 6DoF pose data for event streams. Furthermore, we evaluate the generalization capabilities of two state-of-the-art algorithms, which were pre-trained for the BOP challenge, using our novel YCB-V sequences. the proposed dataset is available at https://***/paroj/ycbev. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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5th chinese conference on pattern recognition and computer vision, PRCV 2022

5th Chinese Conference on Pattern Recognition and Computer V...

引用

5th chinese conference on pattern recognition and computer vision, PRCV 2022

ISBN: (纸本)9783031189098

the proceedings contain 233 papers. the special focus in this conference is on pattern recognition and computer vision. the topics include: An Improved Tensor Network for Image Classification in Histopathology;gradient-Rebalanced Uncertainty Minimization for Cross-Site Adaptation of Medical Image Segmentation;deepEnReg: Joint Enhancement and Affine Registration for Low-contrast Medical Images;fluorescence Microscopy Images Segmentation Based on Prototypical Networks with a Few Annotations;superVessel: Segmenting High-Resolution Vessel from Low-Resolution Retinal Image;Cascade Multiscale Swin-Conv Network for Fast MRI Reconstruction;DEST: Deep Enhanced Swin Transformer Toward Better Scoring for NAFLD;CTCNet: A Bi-directional Cascaded Segmentation Network Combining Transformers with CNNs for Skin Lesions;MR Image Denoising Based on Improved Multipath Matching Pursuit Algorithm;BiDFNet: Bi-decoder and Feedback Network for Automatic Polyp Segmentation with vision Transformers;Statistical Characteristics of 3-D PET Imaging: A Comparison Between Conventional and Total-Body PET Scanners;unsupervised Medical Image Registration Based on Multi-scale Cascade Network;A Novel Local-Global Spatial Attention Network for Cortical Cataract Classification in AS-OCT;PRGAN: A Progressive Refined GAN for Lesion Localization and Segmentation on High-Resolution Retinal Fundus Photography;multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer’s Disease Prediction;robust Liver Segmentation Using Boundary Preserving Dual Attention Network;msFormer: Adaptive Multi-Modality 3D Transformer for Medical Image Segmentation;semi-supervised Medical Image Segmentation with Semantic Distance Distribution Consistency Learning;MultiGAN: Multi-domain Image Translation from OCT to OCTA;TransPND: A Transformer Based Pulmonary Nodule Diagnosis Method on CT Image;FundusGAN: A One-Stage Single Input GAN for Fundus Synthesis;adversarial Learning Based Structural Brain-Network Generat

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5th chinese conference on pattern recognition and computer vision, PRCV 2022