# Let's Transfer Transformations of Shared Semantic Representations

## TL;DR

This paper introduces a method to transfer learned semantic transformations across different image domains without training examples in the target domain, enabling flexible image retrieval based on high-level modifications.

## Contribution

It proposes a novel approach to transfer semantic transformations learned in one domain to another, leveraging shared embeddings and domain accessibility.

## Key findings

- Successful transfer from synthesized images to real images.
- Effective transfer from text descriptions to natural images.
- Improved image retrieval with semantic modifications.

## Abstract

With a good image understanding capability, can we manipulate the images high level semantic representation? Such transformation operation can be used to generate or retrieve similar images but with a desired modification (for example changing beach background to street background); similar ability has been demonstrated in zero shot learning, attribute composition and attribute manipulation image search. In this work we show how one can learn transformations with no training examples by learning them on another domain and then transfer to the target domain. This is feasible if: first, transformation training data is more accessible in the other domain and second, both domains share similar semantics such that one can learn transformations in a shared embedding space. We demonstrate this on an image retrieval task where search query is an image, plus an additional transformation specification (for example: search for images similar to this one but background is a street instead of a beach). In one experiment, we transfer transformation from synthesized 2D blobs image to 3D rendered image, and in the other, we transfer from text domain to natural image domain.