Learning Primitive Relations for Compositional Zero-Shot Learning
This addresses the challenge of compositional generalization in AI for tasks like visual recognition, though it appears incremental as it builds on existing CZSL methods.
The paper tackles the problem of identifying unseen state-object compositions in compositional zero-shot learning by proposing a framework that captures state-object relationships, resulting in outperforming state-of-the-art methods on three benchmark datasets.
Compositional Zero-Shot Learning (CZSL) aims to identify unseen state-object compositions by leveraging knowledge learned from seen compositions. Existing approaches often independently predict states and objects, overlooking their relationships. In this paper, we propose a novel framework, learning primitive relations (LPR), designed to probabilistically capture the relationships between states and objects. By employing the cross-attention mechanism, LPR considers the dependencies between states and objects, enabling the model to infer the likelihood of unseen compositions. Experimental results demonstrate that LPR outperforms state-of-the-art methods on all three CZSL benchmark datasets in both closed-world and open-world settings. Through qualitative analysis, we show that LPR leverages state-object relationships for unseen composition prediction.