On Implicit Attribute Localization for Generalized Zero-Shot Learning

• URL: http://arxiv.org/abs/2103.04704v1
• Date: Mon, 8 Mar 2021 12:31:37 GMT
• Title: On Implicit Attribute Localization for Generalized Zero-Shot Learning
• Authors: Shiqi Yang, Kai Wang, Luis Herranz, Joost van de Weijer
• Abstract summary: We show that common ZSL backbones can implicitly localize attributes, yet this property is not exploited. We then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance.
• Score: 43.61533666141709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline.

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