Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost

ImageNet: A large-scale hierarchical image database

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Large-Scale Machine Learning with Stochastic Gradient Descent

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Aggregating Local Image Descriptors into Compact Codes

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Statistical Pattern Recognition

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Large Scale Online Learning of Image Similarity through Ranking

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What Does Classifying More Than 10,000 Image Categories Tell Us?

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Large-scale image classification: Fast feature extraction and SVM training

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Evaluating knowledge transfer and zero-shot learning in a large-scale setting

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High-dimensional signature compression for large-scale image classification

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Efficient additive kernels via explicit feature maps

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Learning Visual Similarity Measures for Comparing Never Seen Objects

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Learning to rank with (a lot of) word features

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Towards good practice in large-scale learning for image classification

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SIFT-Bag kernel for video event analysis

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Local Features and Kernels for Classication of Texture and Object Categories: A Comprehensive Study

Zhang, J., Lazebnik, S., & Schmid, C. (2006) · Cited by 93

The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories

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LESS: a model-based classifier for sparse subspaces

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Large margin nearest local mean classifier

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ImageNet classification with deep convolutional neural networks

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ImageNet Large Scale Visual Recognition Challenge

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iCaRL: Incremental Classifier and Representation Learning

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DeViSE: A Deep Visual-Semantic Embedding Model

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Towards Open Set Deep Networks

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Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost

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IEEE TRANSACTIONS ON PATTERN RECOGNITION AND MACHINE INTELLIGENCE 1 Distance-Based Image Classification: Generalizing to new classes at near-zero cost

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Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

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Metric learning applied for automatic large scale image classification

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Zero-Shot Image Classification Based on a Learnable Deep Metric

Liu, J., Shi, C., Tu, D., Shi, Z., & Liu, Y. (2021) · Sensors · doi:10.3390/s21093241 · Cited by 11