  Composed Image Retrieval (CIR) is a complex task that retrieves images using
a query, which is configured with an image and a caption that describes desired
modifications to that image. Supervised CIR approaches have shown strong
performance, but their reliance on expensive manually-annotated datasets
restricts their scalability and broader applicability. To address these issues,
previous studies have proposed pseudo-word token-based Zero-Shot CIR (ZS-CIR)
methods, which utilize a projection module to map images to word tokens.
However, we conjecture that this approach has a downside: the projection module
distorts the original image representation and confines the resulting composed
embeddings to the text-side. In order to resolve this, we introduce a novel
ZS-CIR method that uses Spherical Linear Interpolation (Slerp) to directly
merge image and text representations by identifying an intermediate embedding
of both. Furthermore, we introduce Text-Anchored-Tuning (TAT), a method that
fine-tunes the image encoder while keeping the text encoder fixed. TAT closes
the modality gap between images and text, making the Slerp process much more
effective. Notably, the TAT method is not only efficient in terms of the scale
of the training dataset and training time, but it also serves as an excellent
initial checkpoint for training supervised CIR models, thereby highlighting its
wider potential. The integration of the Slerp-based ZS-CIR with a TAT-tuned
model enables our approach to deliver state-of-the-art retrieval performance
across CIR benchmarks.
