Dual Feature Augmentation Network for Generalized Zero-shot Learning
Lei Xiang (Nanjing University of Information Science and Technology ),* Yuan Zhou (Nanjing University of Information Science and Technology), Haoran Duan (Durham University), Yang Long (Durham University)
The 34th British Machine Vision Conference
Abstract
Zero-shot learning (ZSL) aims to infer novel classes without training samples by transferring knowledge from seen classes. Existing embedding-based approaches for ZSL typically employ attention mechanisms to locate attributes on an image. However, these methods often ignore the complex entanglement among different attributes' visual features in the embedding space. Additionally, these methods employ a direct attribute prediction scheme for classification, which does not account for the diversity of attributes in images of the same category. To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features. The visual feature augmentation module explicitly learns attribute features and employs cosine distance to separate them, thus enhancing attribute representation. In the semantic feature augmentation module, we propose a bias learner to capture the offset that bridges the gap between actual and predicted attribute values from a dataset's perspective. Furthermore, we introduce two predictors to reconcile the conflicts between local and global features. Experimental results on three benchmarks demonstrate the marked advancement of our method compared to state-of-the-art approaches. Our code is available at https://github.com/Sion1/DFAN.Video
Citation
@inproceedings{Xiang_2023_BMVC,
author = {Lei Xiang and Yuan Zhou and Haoran Duan and Yang Long},
title = {Dual Feature Augmentation Network for Generalized Zero-shot Learning},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0534.pdf}
}
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