Automatic Semantic Content Removal by Learning to Neglect

TL;DR

This paper presents an integrated system for automatic image content removal and inpainting that jointly segments and fills in regions using a novel encoder-decoder architecture with neglect nodes, trained via conditional GANs.

Contribution

It introduces a unified model that combines segmentation and inpainting in a single pass, reducing errors and outperforming existing methods that require external segmentation.

Findings

Outperforms state-of-the-art inpainting techniques

Joint segmentation and inpainting improves accuracy

Neglect nodes effectively guide the inpainting process

Abstract

We introduce a new system for automatic image content removal and inpainting. Unlike traditional inpainting algorithms, which require advance knowledge of the region to be filled in, our system automatically detects the area to be removed and infilled. Region segmentation and inpainting are performed jointly in a single pass. In this way, potential segmentation errors are more naturally alleviated by the inpainting module. The system is implemented as an encoder-decoder architecture, with two decoder branches, one tasked with segmentation of the foreground region, the other with inpainting. The encoder and the two decoder branches are linked via neglect nodes, which guide the inpainting process in selecting which areas need reconstruction. The whole model is trained using a conditional GAN strategy. Comparative experiments show that our algorithm outperforms state-of-the-art inpainting techniques (which, unlike our system, do not segment the input image and thus must be aided by an external segmentation module.)