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Self-supervised Human Detection and Segmentation via Background Inpainting

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Abstract

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively expensive, we introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera. At the heart of our approach lies the observation that object segmentation and background reconstruction are linked tasks, and that, for structured scenes, background regions can be re-synthesized from their surroundings, whereas regions depicting the moving object cannot. We encode this intuition into a self-supervised loss function that we exploit to train a proposal-based segmentation network. To account for the discrete nature of the proposals, we developed a Monte Carlo-based training strategy that allows the algorithm to explore the large space of object proposals. We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods.

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... Hence, our method relates to self-supervised approaches. We show that the proposed multi-view training increases single-image accuracy performance at inference time, which allows us to outperform state-of-the-art single-view [22,53,9,31,20] and multi-view [37] approaches. Our code is publicly available at https:// github.com/isinsukatircioglu/mvc. ...
... Built upon [43], [9] trains an ensemble of networks, which comes at the cost of requiring significant amounts of additional data. In [20], an inpainting network is trained to identify the regions that are harder to reconstruct from the surrounding image patches and encodes and decodes the content of this region to learn the scene decomposition. [53] employs a similar inpainting network but on flow fields obtained by [44] and aims to generate the mask of a moving object in the region where the inpainting network yields poor reconstruction. ...
... Let us consider the network F of [20], which we use as the backbone of our approach. It takes an image I ∈ R W ×H×3 as input and resynthesizes it. ...
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Katircioglu I, Rhodin H, Spörri J, Salzmann M, Fua P. Human Detection and Segmentation via Multi-view Consensus. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada, 2021, pp. 2855-2864. https://dx.doi.org/10.1109/ICCV48922.2021.00285
... Bielski et al. [2] propose to reposition the generated foreground to disentangle it from the background. Katircioglu et al, [24,25] detect that region as foreground that cannot be inpainted from the surrounding, a strategy previously used on optical flow [63], which however requires a similar background in all training examples or an optical flow estimator. Chen et al. [4] achieved unsupervised foreground segmentation by resampling the foreground appearance to disentangle foreground and background. ...
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Chapter
This accessible new edition explores the major topics in Monte Carlo simulation Simulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo Variance reduction techniques such as the transform likelihood ratio method and the screening method The score function method for sensitivity analysis The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization The cross-entropy method to rare events estimation and combinatorial optimization Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB programs. Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.
Emergence of Object Segmentation in Perturbed Generative Models
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A. Bielski and P. Favaro, "Emergence of Object Segmentation in Perturbed Generative Models," in Advances in Neural Information Processing Systems, 2019.
Unsupervised Object Segmentation by Redrawing
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Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
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A. Kosiorek, H. Kim, Y. W. Teh, and I. Posner, "Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects," in Advances in Neural Information Processing Systems, 2018.
Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks
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