Beyond Seen Primitive Concepts and Attribute-Object Compositional Learning

Learning from seen attribute-object pairs to generalize to unseen compositions has been studied extensively in Compositional Zero-Shot Learning (CZSL). However, CZSL setup is still limited to seen attributes and objects, and cannot generalize to unseen concepts and their compositions. To overcome this limitation, we propose a new task, Open Vocabulary-Compositional Zero-shot Learning (OV-CZSL), where unseen attributes, objects, and unseen compositions are evaluated. To show that OV-CZSL is a challenging yet solvable problem, we propose three new benchmarks based on existing datasets MIT-States, C-GQA and VAW-CZSL, along with new baselines and evaluation setup. We use language embeddings and external vocabulary with our novel neighborhood expansion loss to allow any method to learn semantic correlations between seen and unseen primitives.