Description

.- Computer Vision: Classification.
.- A WEAKLY SUPERVISED PART DETECTION METHOD FOR ROBUST FINE-GRAINED CLASSIFICATION.
.- An Energy Sampling Replay-Based Continual Learning Framework.
.- Coarse-to-Fine Granularity in MultiScale FeatureFusion Network for SAR Ship Classification.
.-Multi-scale convolutional attention fuzzy broad network for few-shot hyperspectral image classification.
.- Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification.
.- Computer Vision: Object Detection.
.- CIA-Net:Cross-modal Interaction and Depth Quality-Aware Network for RGB-D Salient Object Detection.
.- CPH DETR: Comprehensive Regression Loss for End-to-End Object Detection.
.- DecoratingFusion: A LiDAR-Camera Fusion Network with the Combination of Point-level and Feature-level Fusion.
.- EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection.
.- Global-Guided Weighte

Artificial Neural Networks and Machine Learning ICANN 2024

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Paperback by Michael Wand

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.- Computer Vision: Classification..- A WEAKLY SUPERVISED PART DETECTION METHOD FOR ROBUST FINE-GRAINED CLASSIFICATION..- An Energy Sampling Replay-Based Continual Learning... Read more

    Publisher: Springer
    Publication Date: 9/17/2024
    ISBN13: 9783031723346, 978-3031723346
    ISBN10: 3031723341

    Non Fiction , Computing

    Description

    .- Computer Vision: Classification.
    .- A WEAKLY SUPERVISED PART DETECTION METHOD FOR ROBUST FINE-GRAINED CLASSIFICATION.
    .- An Energy Sampling Replay-Based Continual Learning Framework.
    .- Coarse-to-Fine Granularity in MultiScale FeatureFusion Network for SAR Ship Classification.
    .-Multi-scale convolutional attention fuzzy broad network for few-shot hyperspectral image classification.
    .- Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification.
    .- Computer Vision: Object Detection.
    .- CIA-Net:Cross-modal Interaction and Depth Quality-Aware Network for RGB-D Salient Object Detection.
    .- CPH DETR: Comprehensive Regression Loss for End-to-End Object Detection.
    .- DecoratingFusion: A LiDAR-Camera Fusion Network with the Combination of Point-level and Feature-level Fusion.
    .- EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection.
    .- Global-Guided Weighte

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