Compositionality, the ability to combine existing concepts and generalize
towards novel compositions, is a key functionality for intelligent entities.
Here, we study the problem of Compositional Zero-Shot Learning (CZSL), which
aims at recognizing novel attribute-object compositions. Recent approaches
build their systems on top of large-scale Vision-Language Pre-trained (VLP)
models, e.g. CLIP, and observe significant improvements. However, these methods
treat CLIP as a black box and focus on pre- and post-CLIP operations. Here, we
propose to dive deep into the architecture and insert adapters, a
parameter-efficient technique proven to be effective among large language
models, to each CLIP encoder layer. We further equip adapters with concept
awareness so that concept-specific features of "object", "attribute" and
"composition" can be extracted. We name our method CAILA, Concept-Aware
Intra-Layer Adapters. Quantitative evaluations performed on three popular CZSL
datasets, MIT-States, C-GQA, and UT-Zappos, reveal that CAILA achieves
double-digit relative improvements against the current state-of-the-art on all
benchmarks.

研究了组合零样本学习 (Compositional Zero-Shot Learning, CZSL) 的问题，并提出了一种新方法 CAILA，其中使用结构插座，增加了对 “对象”，“属性” 和 “组合” 的概念的知觉，相对于现有技术，CAILA 在 MIT-States，C-GQA 和 UT-Zappos 数据集上都有两位数的改进。