Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning
<|reference_start|>Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning: To transfer knowledge from seen attribute-object compositions to recognize unseen ones, recent compositional zero-shot learning (CZSL) methods mainly discuss the optimal classification branches to identify the elements, leading to the popularity of employing a three-branch architecture. However, these methods mix up the underlying relationship among the branches, in the aspect of consistency and diversity. Specifically, consistently providing the highest-level features for all three branches increases the difficulty in distinguishing classes that are superficially similar. Furthermore, a single branch may focus on suboptimal regions when spatial messages are not shared between the personalized branches. Recognizing these issues and endeavoring to address them, we propose a novel method called Focus-Consistent Multi-Level Aggregation (FOMA). Our method incorporates a Multi-Level Feature Aggregation (MFA) module to generate personalized features for each branch based on the image content. Additionally, a Focus-Consistent Constraint encourages a consistent focus on the informative regions, thereby implicitly exchanging spatial information between all branches. Extensive experiments on three benchmark datasets (UT-Zappos, C-GQA, and Clothing16K) demonstrate that our FOMA outperforms SOTA.<|reference_end|>
@article{dai2024focus-consistent,
    title={Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot
  Learning},
    author={Fengyuan Dai, Siteng Huang, Min Zhang, Biao Gong, Donglin Wang},
    journal={arXiv preprint arXiv:2408.17083},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17083},
    primaryClass={cs.CV}
}