Proceedings ArticleDOI
Large scale metric learning from equivalence constraints
Martin Köstinger,Martin Hirzer,Paul Wohlhart,Peter M. Roth,Horst Bischof +4 more
- pp 2288-2295
TLDR
This paper introduces a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective, which is orders of magnitudes faster than comparable methods.Abstract:
In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective. In contrast to existing methods we do not rely on complex optimization problems requiring computationally expensive iterations. Hence, our method is orders of magnitudes faster than comparable methods. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include faces in unconstrained environments, matching before unseen object instances and person re-identification across spatially disjoint cameras. In the latter two benchmarks we clearly outperform the state-of-the-art.read more
Citations
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Proceedings ArticleDOI
Scalable Person Re-identification: A Benchmark
TL;DR: A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.
Proceedings ArticleDOI
Unsupervised Feature Learning via Non-parametric Instance Discrimination
TL;DR: This work forms this intuition as a non-parametric classification problem at the instance-level, and uses noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes.
Proceedings ArticleDOI
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
TL;DR: A novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter is proposed and significantly outperforms state-of-the-art methods on this dataset.
Proceedings ArticleDOI
SphereFace: Deep Hypersphere Embedding for Face Recognition
TL;DR: In this paper, the angular softmax (A-softmax) loss was proposed to learn angularly discriminative features for deep face recognition under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal interclass distance under a suitably chosen metric space.
Proceedings ArticleDOI
Person re-identification by Local Maximal Occurrence representation and metric learning
TL;DR: This paper proposes an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA), and presents a practical computation method for XQDA.
References
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Proceedings Article
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