  Composed Image Retrieval (CIR) involves searching for target images based on
an image-text pair query. While current methods treat this as a query-target
matching problem, we argue that CIR triplets contain additional associations
beyond this primary relation. In our paper, we identify two new relations
within triplets, treating each triplet as a graph node. Firstly, we introduce
the concept of text-bridged image alignment, where the query text serves as a
bridge between the query image and the target image. We propose a hinge-based
cross-attention mechanism to incorporate this relation into network learning.
Secondly, we explore complementary text reasoning, considering CIR as a form of
cross-modal retrieval where two images compose to reason about complementary
text. To integrate these perspectives effectively, we design a twin
attention-based compositor. By combining these complementary associations with
the explicit query pair-target image relation, we establish a comprehensive set
of constraints for CIR. Our framework, CaLa (Complementary Association Learning
for Augmenting Composed Image Retrieval), leverages these insights. We evaluate
CaLa on CIRR and FashionIQ benchmarks with multiple backbones, demonstrating
its superiority in composed image retrieval.
