Multi-ColorGAN: Few-shot vehicle recoloring via memory-augmented networks

Abstract

Despite the notable successes of Generative adversarial networks (GANs) achieved to date, applying them to real-world problems still poses significant challenges. In real traffic surveillance scenarios, for the task of generating images of multiple color of truck heads and cars without changing textures and license plates, conditional image generation hardly manipulate the generated images by the color attribute. Image style transfer methods inevitably produce color smearing. Even state-of-the-art methods of disentangled representation learning (e.g. MixNMatch) cannot disentangle colors individually, ensuring that irrelevant factors, such as texture remain the same. To solve this problem, we present an approach called Multi-ColorGAN based on memory-augmented networks for multi-color real vehicle coloring/generation with limited data. In particular, our model could filter out unwanted color changes in specific areas with a simple but effective method called Fusion Module, and generate more natural color images. Experiments on three vehicle image benchmarks and a new truck image dataset are conducted to evaluate the proposed Multi-ColorGAN compared to state-of-the-art.