Refining Visual Representation for Generalized Zero-Shot Recognition through Implicit-Semantics-Guided Metric Learning

YanHe Chen, Mei-Chen Yeh

Keywords: generalized zero-shot learning, metric learning, multi-class classification

Abstract: Deep metric learning (DML) is effective to address the large intra- and the small inter-class variation problem in visual recognition; however, when applied for generalized zero-shot learning (GZSL) in which the label of a target image may belong to an unseen category, this technique can be easily biased towards seen classes. Alternatively in GZSL some form of semantic space is available, which plays an important role in relating seen and unseen classes and is widely used to guide the learning of visual representation. To take advantage of DML while avoiding overfitting to seen classes, we propose a novel representation learning framework$\textemdash$Metric Learning with Implicit Semantics (MLIS)$\textemdash$to refine discriminative and generalizable visual features for GZSL. Specifically, we disentangle the effects of semantics on feature extractor and image classification of the model, so that semantics only participate in feature learning, and classification only uses the refined visual features. We further relax the visual-semantic alignment requirement, avoiding performing pair-wise comparisons between the image and the class embeddings. Experimental results demonstrate that the proposed MLIS framework bridges DML and GZSL. It achieves state-of-the-art performance, and is robust and flexible to the integration with several metric learning based loss functions.

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