Pluralistic Image Completion

Most image completion methods produce only one result for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for \textbf{pluralistic image completion} -- the task of generating multiple and diverse plausible solutions for image completion. A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higher-quality completion results, but also with multiple and diverse plausible outputs.Comment: 21 pages, 16 figure

Unpacking the client(s): constructions, positions and client–consultant dynamics

Research on management consultancy usually emphasizes the role and perspective of the consultants. Whilst important, consultants are only one element in a dynamic relationship involving both consultants and their clients. In much of the literature, the client is neglected, or is assumed to represent a distinct, immutable entity. In this paper, we argue that the client organisation is not uniform but is instead (like organisations generally) a more or less heterogeneous assemblage of actors, interests and inclinations involved in multiple and varied ways in consultancy projects. This paper draws upon three empirical cases and emphasizes three key aspects of clients in the context of consultancy projects: (a) client diversity, including, but not limited to diversity arising solely from (pre-)structured contact relations and interests; (b) processes of constructing ‘the client’ (including negotiation, conflict, and reconstruction) and the client identities which are thereby produced; and (c) the dynamics of client–consultant relations and how these influence the construction of multiple and perhaps contested client positions and identities

Inclusion ideals and inclusion problems: Parsons and Luhmann on religion and secularization

This paper builds upon the theoretical work of Talcott Parsons and Niklas Luhmann and offers a critical reconstruction of their views on religion (Christianity) and secularization in the western world. It discusses the relation between the functional differentiation of modern society, the individualization of inclusion imperatives and the changing expectations regarding inclusion/exclusion in religious communication. From this perspective, it analyzes secularization in terms of perceived problems of inclusion in religious communication, and in terms of the reactions of Christian religions to these perceived problems. It thereby shows how the theories of Parsons and Luhmann are useful for empirical and historical research, and how they open up new perspectives for empirical and historical research

HiMFR: A Hybrid Masked Face Recognition Through Face Inpainting

To recognize the masked face, one of the possible solutions could be to restore the occluded part of the face first and then apply the face recognition method. Inspired by the recent image inpainting methods, we propose an end-to-end hybrid masked face recognition system, namely HiMFR, consisting of three significant parts: masked face detector, face inpainting, and face recognition. The masked face detector module applies a pretrained Vision Transformer (ViT\_b32) to detect whether faces are covered with masked or not. The inpainting module uses a fine-tune image inpainting model based on a Generative Adversarial Network (GAN) to restore faces. Finally, the hybrid face recognition module based on ViT with an EfficientNetB3 backbone recognizes the faces. We have implemented and evaluated our proposed method on four different publicly available datasets: CelebA, SSDMNV2, MAFA, {Pubfig83} with our locally collected small dataset, namely Face5. Comprehensive experimental results show the efficacy of the proposed HiMFR method with competitive performance. Code is available at https://github.com/mdhosen/HiMFRComment: 7 pages, 6 figures, International Conference on Pattern Recognition Workshop: Deep Learning for Visual Detection and Recognitio

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

Recent studies have shown the importance of modeling long-range interactions in the inpainting problem. To achieve this goal, existing approaches exploit either standalone attention techniques or transformers, but usually under a low resolution in consideration of computational cost. In this paper, we present a novel transformer-based model for large hole inpainting, which unifies the merits of transformers and convolutions to efficiently process high-resolution images. We carefully design each component of our framework to guarantee the high fidelity and diversity of recovered images. Specifically, we customize an inpainting-oriented transformer block, where the attention module aggregates non-local information only from partial valid tokens, indicated by a dynamic mask. Extensive experiments demonstrate the state-of-the-art performance of the new model on multiple benchmark datasets. Code is released at https://github.com/fenglinglwb/MAT.Comment: Accepted to CVPR2022 Ora

Collaborative systems for enhancing the analysis of social surveys: the grid enabled specialist data environments

This paper describes a group of online services which are designed to support social survey research and the production of statistical results. The 'Grid Enabled Specialist Data Environment' (GESDE) services constitute three related systems which offer facilities to search for, extract and exploit supplementary data and metadata concerned with the measurement and operationalisation of survey variables. The services also offer users the opportunity to deposit and distribute their own supplementary data resources for the benefit of dissemination and replication of the details of their own analysis. The GESDE services focus upon three application areas: specialist data relating to the measurement of occupations; educational qualifications; and ethnicity (including nationality, language, religion, national identity). They identify information resources related to the operationalisation of variables which seek to measure each of these concepts - examples include coding frames, crosswalk and translation files, and standardisation and harmonisation recommendations. These resources constitute important supplementary data which can be usefully exploited in the analysis of survey data. The GESDE services work by collecting together as much of this supplementary data as possible, and making it searchable and retrievable to others. This paper discusses the current features of the GESDE services (which have been designed as part of a wider programme of ‘e-Science’ research in the UK), and considers ongoing challenges in providing effective support for variable-oriented statistical analysis in the social sciences

Current Literature

Material appearing below is thought to be of particular interest to Linacre Quarterly readers because of its moral, religious, or philosophic content. The medical literature constitutes the primary but not the sole source of such material. In general, abstracts\u27 are intended to reflect the substance of the original article. Contributions and comments from readers are invited. (E. C. Laforet, M.D. , 2000 Washington St., Newton Lower Falls, MA. 02162.

Zoom-to-Inpaint: Image Inpainting with High-Frequency Details

Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details. In this paper, we propose applying super-resolution to coarsely reconstructed outputs, refining them at high resolution, and then downscaling the output to the original resolution. By introducing high-resolution images to the refinement network, our framework is able to reconstruct finer details that are usually smoothed out due to spectral bias - the tendency of neural networks to reconstruct low frequencies better than high frequencies. To assist training the refinement network on large upscaled holes, we propose a progressive learning technique in which the size of the missing regions increases as training progresses. Our zoom-in, refine and zoom-out strategy, combined with high-resolution supervision and progressive learning, constitutes a framework-agnostic approach for enhancing high-frequency details that can be applied to any CNN-based inpainting method. We provide qualitative and quantitative evaluations along with an ablation analysis to show the effectiveness of our approach. This seemingly simple, yet powerful approach, outperforms state-of-the-art inpainting methods

Text-Guided Neural Image Inpainting

Image inpainting task requires filling the corrupted image with contents coherent with the context. This research field has achieved promising progress by using neural image inpainting methods. Nevertheless, there is still a critical challenge in guessing the missed content with only the context pixels. The goal of this paper is to fill the semantic information in corrupted images according to the provided descriptive text. Unique from existing text-guided image generation works, the inpainting models are required to compare the semantic content of the given text and the remaining part of the image, then find out the semantic content that should be filled for missing part. To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet). Firstly, a dual multimodal attention mechanism is designed to extract the explicit semantic information about the corrupted regions, which is done by comparing the descriptive text and complementary image areas through reciprocal attention. Secondly, an image-text matching loss is applied to maximize the semantic similarity of the generated image and the text. Experiments are conducted on two open datasets. Results show that the proposed TDANet model reaches new state-of-the-art on both quantitative and qualitative measures. Result analysis suggests that the generated images are consistent with the guidance text, enabling the generation of various results by providing different descriptions. Codes are available at https://github.com/idealwhite/TDANetComment: ACM MM'2020 (Oral). 9 pages, 4 tables, 7 figure