The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Il...
ISBN:
(纸本)9783031781940
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remotesensingimages;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Il...
ISBN:
(纸本)9783031783821
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remotesensingimages;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Il...
ISBN:
(纸本)9783031781278
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remotesensingimages;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Il...
ISBN:
(纸本)9783031781858
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remotesensingimages;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Il...
ISBN:
(纸本)9783031783531
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remotesensingimages;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Il...
ISBN:
(纸本)9783031784439
The proceedings contain 958 papers. The special focus in this conference is on patternrecognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remotesensingimages;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and
Recently, with the increasing number of large-scale remotesensingimages, the demand for large-scale remotesensingimage object classification is growing and attracting the interest of many researchers. Hashing, bec...
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ISBN:
(纸本)9781728188089
Recently, with the increasing number of large-scale remotesensingimages, the demand for large-scale remotesensingimage object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has widely solve the problem of large-scale remotesensingimage. Supervised hashing methods mainly leverage the label information of remotesensingimage to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remotesensingimage. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.
In recent years, video streaming services have become increasingly popular. In general, the search function in a video sharing service site evaluates the relevance of a search query to the title, tags, description, an...
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ISBN:
(纸本)9781665462198
In recent years, video streaming services have become increasingly popular. In general, the search function in a video sharing service site evaluates the relevance of a search query to the title, tags, description, and so on given by the creator of the video. Then, the search results with the highest relevance are displayed. Therefore, if a title is given to a video that does not match its content, there is a possibility that a video with low relevance will be found. In this research, ( 1) we built a new system that retrieves animal videos that are relevant to its content using imagerecognition. (2) By describing the relationships between the concepts of animal families and species and incorporating them into the retrieval system, it is possible to retrieve animal videos by their family names. Adding retrieval by animal family name enabled us to find species that have not been learned. In this research, (3) we confirmed the usefulness of our video retrieval system using trained neural networks, GoogLeNet and ResNet50, as animal species classifiers.
Polarimetric synthetic aperture radar (PolSAR) image classification is a dynamic and important topic in remotesensingimage interpretation. However, most deep learning models require enough labeled samples to achieve...
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Centralized aerial imagery analysis techniques face two challenges. The first one is the data silos problem, where data is located at different organizations separately. The second challenge is the class imbalance in ...
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ISBN:
(纸本)9789819984619;9789819984626
Centralized aerial imagery analysis techniques face two challenges. The first one is the data silos problem, where data is located at different organizations separately. The second challenge is the class imbalance in the overall distribution of aerial scene data, due to the various collecting procedures across organizations. Federated learning (FL) is a method that allows multiple organizations to learn collaboratively from their local data without sharing. This preserves users' privacy and tackles the data silos problem. However, traditional FL methods assume that the datasets are globally balanced, which is not realistic for aerial imagery applications. In this paper, we propose a Two-Stage FL framework (TS-FL), which mitigate the effect of the class imbalanced problem in aerial scene classification under FL. In particular, the framework introduces a feature representation method by combing supervised contrastive learning with knowledge distillation to enhance the model's feature representation ability and minimize the client drift. Experiments on two public aerial datasets demonstrate that the proposed method outperforms other FL methods and possesses good generalization ability.
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