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.

通过引入Grounding DINO并结合组合式软提示（Compositional Soft Prompting）和组合式预期（Compositional Anticipation）来增强物体检测的组合式零样本学习（Compositional Zero-Shot Learning），并通过对类似组合的模型混淆进行增量式调整，提供了一种在有限数据下学习各种组合的框架，并在额外数据可用时提高性能。

无需遗忘的预期未来物体组合