Open AccessPosted Content
Learning Fine-grained Image Similarity with Deep Ranking
Jiang Wang,Yang Song,Thomas Leung,Charles J. Rosenberg,Jinbin Wang,James Philbin,Bo Chen,Ying Wu +7 more
TLDR
A deep ranking model that employs deep learning techniques to learn similarity metric directly from images has higher learning capability than models based on hand-crafted features and deep classification models.Abstract:
Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from this http URL has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models.read more
Citations
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FaceNet: A unified embedding for face recognition and clustering
TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
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Deep Metric Learning via Lifted Structured Feature Embedding
TL;DR: In this article, the authors propose to lift the vector of pairwise distances within the batch to the matrix of pairswise distances, which enables the algorithm to learn the state-of-the-art feature embedding by optimizing a novel structured prediction objective on the lifted problem.
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Improved deep metric learning with multi-class N-pair loss objective
TL;DR: This paper proposes a new metric learning objective called multi-class N-pair loss, which generalizes triplet loss by allowing joint comparison among more than one negative examples and reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples.
Proceedings ArticleDOI
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
TL;DR: A novel multi-channel parts-based convolutional neural network model under the triplet framework for person re-identification that significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based ones, on the challenging i-LIDS, VIPeR, PRID2011 and CUHK01 datasets.
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SphereFace: Deep Hypersphere Embedding for Face Recognition
TL;DR: This paper proposes the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features in deep face recognition (FR) problem under open-set protocol.
References
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Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
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Proceedings ArticleDOI
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TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
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Improving neural networks by preventing co-adaptation of feature detectors
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