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One Shot Similarity Metric Learning for Action Recognition
This video was recorded at 1st International Workshop on Similarity-Based Pattern Analysis and Recognition. The One-Shot-Similarity (OSS) is a framework for classifier-based 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
