Machine Learning
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Large Scale Online Learning of Image Similarity Through Ranking
The Journal of Machine Learning Research
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Identifying Join Candidates in the Cairo Genizah
International Journal of Computer Vision
The Action Similarity Labeling Challenge
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Face recognition in unconstrained videos with matched background similarity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Motion interchange patterns for action recognition in unconstrained videos
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Exploring the similarities of neighboring spatiotemporal points for action pair matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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The One-Shot-Similarity (OSS) is a framework for classifierbased similarity functions. It is based on the use of background samples and was shown to excel in tasks ranging from face recognition to document analysis. However, we found that its performance depends on the ability to effectively learn the underlying classifiers, which in turn depends on the underlying metric. In this work we present a metric learning technique that is geared toward improved OSS performance. We test the proposed technique using the recently presented ASLAN action similarity labeling benchmark. Enhanced, state of the art performance is obtained, and the method compares favorably to leading similarity learning techniques.