2022/05/30 · Therefore, we propose a content-attribute disentanglement architecture which separates the content and attribute information of images. ... With ...

2024/02/02 · With extensive experiments, we show that our method achieves state-of-the-art and competitive results on four benchmark datasets in GZSL. Our ...

Corresponding author: Jihie Kim (e-mail: jihie.kim@dgu.edu). Yoojin An and Sangyeon Kim contributed equally to this work and are co-first authors.

2024/09/12 · The key challenge in zero-shot learning is inferring latent semantic knowledge between visual and attribute features of seen classes to achieve ...

Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not ob- servable during training.

2021/01/20 · In this paper, we propose a novel semantics disentangling framework for the generalized zero-shot learning task (SDGZSL), where the visual features of unseen ...

Zero-Shot Learning (ZSL) aims to recognize images belong- ing to unseen classes that are unavailable in the training process, while Generalized Zero-Shot ...

[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning) - uqzhichen/SDGZSL.

Illustration of similarities among attribute codes on AWA2. Content-Attribute Disentanglement for Generalized Zero-Shot Learning. Article. Full-text available.

In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the ...