Automatic Semantic Content Removal by Learning to Neglect
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.)
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderSegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Decoupling Degradation and Content Processing for Adverse Weather Image Restoration
Adverse weather image restoration strives to recover clear images from those affected by various weather types, such as rain, haze, and snow. Each weather type calls for a tailored degradation removal approach due to its…
Image RestorationHierarchical Semantic-Visual Fusion of Visible and Near-infrared Images for Long-range Haze Removal
While image dehazing has advanced substantially in the past decade, most efforts have focused on short-range scenarios, leaving long-range haze removal under-explored. As distance increases, intensified scattering leads …
Image DehazingMarkCleaner: High-Fidelity Watermark Removal via Imperceptible Micro-Geometric Perturbation
Semantic watermarks exhibit strong robustness against conventional image-space attacks. In this work, we show that such robustness does not survive under micro-geometric perturbations: spatial displacements can remove wa…
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks
Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shado…
DecoderImage SegmentationImage Shadow RemovalQuantization+3Towards Robust Content Watermarking Against Removal and Forgery Attacks
Generated contents have raised serious concerns about copyright protection, image provenance, and credit attribution. A potential solution for these problems is watermarking. Recently, content watermarking for text-to-im…