Blind image inpainting is extremely challenging due to the unknown and multi-property complexity of contamination in different contaminated images. Current mainstream work decomposes blind image inpainting into two stages: mask estimating from the contaminated image and image inpainting based on the estimated mask, and this two-stage solution involves two CNN-based encoder-decoder architectures for estimating and inpainting separately. In this work, we propose a novel one-stage Transformer-CNN Hybrid AutoEncoder (TransCNN-HAE) for blind image inpainting, which intuitively follows the inpainting-then-reconstructing pipeline by leveraging global long-range contextual modeling of Transformer to repair contaminated regions and local short-range contextual modeling of CNN to reconstruct the repaired image. Moreover, a Cross-layer Dissimilarity Prompt (CDP) is devised to accelerate the identifying and inpainting of contaminated regions. Ablation studies validate the efficacy of both TransCNN-HAE and CDP, and extensive experiments on various datasets with multi-property contaminations show that our method achieves state-of-the-art performance with much lower computational cost on blind image inpainting. Our code is available at https://github.com/zhenglab/TransCNN-HAE.

Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. A line of works, called the clustering of bandits (CLUB), utilize the collaborative effect over users and dramatically improve the recommendation quality. Owing to the increasing application scale and public concerns about privacy, there is a growing demand to keep user data decentralized and push bandit learning to the local server side. Existing CLUB algorithms, however, are designed under the centralized setting where data are available at a central server. We focus on studying the federated online clustering of bandit (FCLUB) problem, which aims to minimize the total regret while satisfying privacy and communication considerations. We design a new phase-based scheme for cluster detection and a novel asynchronous communication protocol for cooperative bandit learning for this problem. To protect users’ privacy, previous differential privacy (DP) definitions are not very suitable, and we propose a new DP notion that acts on the user cluster level. We provide rigorous proofs to show that our algorithm simultaneously achieves (clustered) DP, sublinear communication complexity and sublinear regret. Finally, experimental evaluations show our superior performance compared with benchmark algorithms.

This paper studies the problem of painting the whole image from part of it, namely painting from part or part-painting for short, involving both inpainting and outpainting. To address the challenge of taking full advantage of both information from local domain (part) and knowledge from global domain (dataset), we propose a novel part-painting method according to the observations of relationship between part and whole, which consists of three stages: part-noise restarting, part-feature repainting, and part-patch refining, to paint the whole image by leveraging both feature-level and patch-level part as well as powerful representation ability of generative adversarial network. Extensive ablation studies show efficacy of each stage, and our method achieves state-of-the-art performance on both inpainting and outpainting benchmarks with free-form parts, including our new mask dataset for irregular outpainting. Our code and dataset are available at https://github.com/zhenglab/partpainting.