TY  - JOUR
AU  - Shiqi Yang
AU  - Kai Wang
AU  - Luis Herranz
AU  - Joost Van de Weijer
PY  - 2021//
TI  - On Implicit Attribute Localization for Generalized Zero-Shot Learning
JO  - IEEE Signal Processing Letters
SP  - 872
EP  - 876
VL  - 28
N2  - 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.
UR  - https://ieeexplore.ieee.org/document/9405481
L1  - http://refbase.cvc.uab.es/files/YWH2021.pdf
UR  - http://dx.doi.org/10.1109/LSP.2021.3073655
N1  - LAMP; 600.120
ID  - Shiqi Yang2021
ER  -