Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning
<|reference_start|>Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning: Compositional Zero-Shot Learning (CZSL) aims to predict unknown compositions made up of attribute and object pairs. Predicting compositions unseen during training is a challenging task. We are exploring Open World Compositional Zero-Shot Learning (OW-CZSL) in this study, where our test space encompasses all potential combinations of attributes and objects. Our approach involves utilizing the self-attention mechanism between attributes and objects to achieve better generalization from seen to unseen compositions. Utilizing a self-attention mechanism facilitates the model's ability to identify relationships between attribute and objects. The similarity between the self-attended textual and visual features is subsequently calculated to generate predictions during the inference phase. The potential test space may encompass implausible object-attribute combinations arising from unrestricted attribute-object pairings. To mitigate this issue, we leverage external knowledge from ConceptNet to restrict the test space to realistic compositions. Our proposed model, Attention-based Simple Primitives (ASP), demonstrates competitive performance, achieving results comparable to the state-of-the-art.<|reference_end|>
@article{munir2024attention,
    title={Attention Based Simple Primitives for Open World Compositional Zero-Shot
  Learning},
    author={Ans Munir, Faisal Z. Qureshi, Muhammad Haris Khan, Mohsen Ali},
    journal={arXiv preprint arXiv:2407.13715},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13715},
    primaryClass={cs.CV cs.LG}
}