Disentangling Before Composing : Learning Invariant Disentangled Features for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and its corresponding composition into a common embedding space to measure their compatibility scor...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - PP(2024) vom: 28. Okt. |
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| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and its corresponding composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen compositions. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant features to recognize attributes attached to any object reliably, and vice versa. Specifically, we propose an invariant feature learning framework to align different domains at the representation and gradient levels to capture the intrinsic characteristics associated with the tasks. To further facilitate and encourage the disentanglement of attributes and objects, we propose an "encoding-reshuffling-decoding" process to help the model avoid spurious correlations by randomly regrouping the disentangled features into synthetic features. Ultimately, our method improves generalization by learning to disentangle features that represent two independent factors of attributes and objects. Experiments demonstrate that the proposed method achieves state-of-the-art or competitive performance in both closed-world and open-world scenarios. Codes are available at https://github.com/PRIS-CV/Disentangling-before-Composing |
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| Beschreibung: | Date Revised 28.10.2024 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2024.3487222 |