Zero-Shot Recognition through Image-Guided Semantic Classification

• URL: http://arxiv.org/abs/2007.11814v1
• Date: Thu, 23 Jul 2020 06:22:40 GMT
• Title: Zero-Shot Recognition through Image-Guided Semantic Classification
• Authors: Mei-Chen Yeh and Fang Li
• Abstract summary: We present a new embedding-based framework for zero-shot learning (ZSL) Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification.
• Score: 9.291055558504588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new embedding-based framework for zero-shot learning (ZSL). Most embedding-based methods aim to learn the correspondence between an image classifier (visual representation) and its class prototype (semantic representation) for each class. Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. Given an input image, the proposed Image-Guided Semantic Classification (IGSC) method creates a label classifier, being applied to all label embeddings to determine whether a label belongs to the input image. Therefore, semantic classifiers are image-adaptive and are generated during inference. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms state-of-the-art embedding-based generalized ZSL approaches on standard benchmarks.

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