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Papers › Symmetry and Group in Attribute-Object Compositions

Symmetry and Group in Attribute-Object Compositions

1 Apr 2020CVPR 2020 6arXiv:2004.00587archive 2025-07-28

Yong-Lu Li, Yue Xu, Xiaohan Mao, Cewu Lu

Attributes and objects can compose diverse compositions. To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee the rationality. In this paper, we first propose a previously ignored principle of attribute-object transformation: Symmetry. For example, coupling peeled-apple with attribute peeled should result in peeled-apple, and decoupling peeled from apple should still output apple. Incorporating the symmetry principle, a transformation framework inspired by group theory is built, i.e. SymNet. SymNet consists of two modules, Coupling Network and Decoupling Network. With the group axioms and symmetry property as objectives, we adopt Deep Neural Networks to implement SymNet and train it in an end-to-end paradigm. Moreover, we propose a Relative Moving Distance (RMD) based recognition method to utilize the attribute change instead of the attribute pattern itself to classify attributes. Our symmetry learning can be utilized for the Compositional Zero-Shot Learning task and outperforms the state-of-the-art on widely-used benchmarks. Code is available at https://github.com/DirtyHarryLYL/SymNet.

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DirtyHarryLYL/SymNet officialmentioned in papermentioned on GitHubtfApache-2.0 report

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Tasks

AttributeCompositional Zero-Shot LearningObjectZero-Shot Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Compositional Zero-Shot Learning MIT-States SymNet Top-1 accuracy % 19.9 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States SymNet Top-2 accuracy % 28.2 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States SymNet Top-3 accuracy % 33.8 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet H-Mean 16.1 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Seen accuracy 24.4 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Test AUC top 1 3.0 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Test AUC top 2 7.6 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Test AUC top 3 12.3 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Unseen accuracy 25.2 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Val AUC top 1 4.3 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Val AUC top 2 9.8 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning MIT-States, generalized split SymNet Val AUC top 3 14.8 #2 of 2 Archive leaderboard report
Compositional Zero-Shot Learning UT-Zappos SymNet Top-1 accuracy % 52.1 #1 of 1 Archive leaderboard report
Compositional Zero-Shot Learning UT-Zappos SymNet Top-2 accuracy % 67.8 #1 of 1 Archive leaderboard report
Compositional Zero-Shot Learning UT-Zappos SymNet Top-3 accuracy % 76.0 #1 of 1 Archive leaderboard report

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