  Despite the significant advancements in computer vision models, their ability
to generalize to novel object-attribute compositions remains limited. Existing
methods for Compositional Zero-Shot Learning (CZSL) mainly focus on image
classification. This paper aims to enhance CZSL in object detection without
forgetting prior learned knowledge. We use Grounding DINO and incorporate
Compositional Soft Prompting (CSP) into it and extend it with Compositional
Anticipation. We achieve a 70.5% improvement over CSP on the harmonic mean (HM)
between seen and unseen compositions on the CLEVR dataset. Furthermore, we
introduce Contrastive Prompt Tuning to incrementally address model confusion
between similar compositions. We demonstrate the effectiveness of this method
and achieve an increase of 14.5% in HM across the pretrain, increment, and
unseen sets. Collectively, these methods provide a framework for learning
various compositions with limited data, as well as improving the performance of
underperforming compositions when additional data becomes available.
