  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.
