# Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks

## TL;DR

This paper introduces a bilinear residual layer within cGANs to enhance language-based image editing, enabling more powerful representations and higher quality generated images.

## Contribution

The paper proposes a novel bilinear residual layer for cGANs that captures second-order correlations, improving image editing quality in LBIE tasks.

## Key findings

- Generated images are of higher quality than previous methods.
- The bilinear residual layer effectively captures complex semantic correlations.
- Quantitative results show improved editing accuracy.

## Abstract

The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description. The main challenge of LBIE is to disentangle the semantics in image and text and then combine them to generate realistic images. Therefore, the editing performance is heavily dependent on the learned representation. In this work, conditional generative adversarial network (cGAN) is utilized for LBIE. We find that existing conditioning methods in cGAN lack of representation power as they cannot learn the second-order correlation between two conditioning vectors. To solve this problem, we propose an improved conditional layer named Bilinear Residual Layer (BRL) to learning more powerful representations for LBIE task. Qualitative and quantitative comparisons demonstrate that our method can generate images with higher quality when compared to previous LBIE techniques.