Summary of Hybrid Discriminative Attribute-object Embedding Network For Compositional Zero-shot Learning, by Yang Liu et al.
Hybrid Discriminative Attribute-Object Embedding Network for Compositional Zero-Shot Learning
by Yang Liu, Xinshuo Wang, Jiale Du, Xinbo Gao, Jungong Han
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel method called Hybrid Discriminative Attribute-Object Embedding (HDA-OE) network for compositional zero-shot learning (CZSL). CZSL recognizes new combinations by learning from known attribute-object pairs. The main challenge of this task lies in the complex interactions between attributes and object visual representations, which lead to significant differences in images. To address these problems, HDA-OE introduces an attribute-driven data synthesis (ADDS) module to increase the variability of training data and a subclass-driven discriminative embedding (SDDE) module to enhance the subclass discriminative ability of the encoding. The proposed model has been evaluated on three benchmark datasets, and the results verify its effectiveness and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists develop a new way to learn called Compositional Zero-Shot Learning (CZSL). CZSL lets us recognize new things by learning from what we already know about attributes and objects. The challenge is that there are many different interactions between these attributes and object pictures, which makes it hard for the computer to understand. To fix this, they created a special network called HDA-OE that uses two new modules: ADDS and SDDE. These modules help the network learn from more diverse data and capture subtle differences between attributes. They tested their model on three datasets and found that it works really well. |
Keywords
» Artificial intelligence » Embedding » Zero shot
