Exploiting Semantic Attributes for Transductive Zero-Shot Learning
- URL: http://arxiv.org/abs/2303.09849v1
- Date: Fri, 17 Mar 2023 09:09:48 GMT
- Title: Exploiting Semantic Attributes for Transductive Zero-Shot Learning
- Authors: Zhengbo Wang, Jian Liang, Zilei Wang, Tieniu Tan
- Abstract summary: Zero-shot learning aims to recognize unseen classes by generalizing the relation between visual features and semantic attributes learned from the seen classes.
We present a novel transductive ZSL method that produces semantic attributes of the unseen data and imposes them on the generative process.
Experiments on five standard benchmarks show that our method yields state-of-the-art results for zero-shot learning.
- Score: 97.61371730534258
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Zero-shot learning (ZSL) aims to recognize unseen classes by generalizing the
relation between visual features and semantic attributes learned from the seen
classes. A recent paradigm called transductive zero-shot learning further
leverages unlabeled unseen data during training and has obtained impressive
results. These methods always synthesize unseen features from attributes
through a generative adversarial network to mitigate the bias towards seen
classes. However, they neglect the semantic information in the unlabeled unseen
data and thus fail to generate high-fidelity attribute-consistent unseen
features. To address this issue, we present a novel transductive ZSL method
that produces semantic attributes of the unseen data and imposes them on the
generative process. In particular, we first train an attribute decoder that
learns the mapping from visual features to semantic attributes. Then, from the
attribute decoder, we obtain pseudo-attributes of unlabeled data and integrate
them into the generative model, which helps capture the detailed differences
within unseen classes so as to synthesize more discriminative features.
Experiments on five standard benchmarks show that our method yields
state-of-the-art results for zero-shot learning.
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