In this paper, we introduce a comprehensive benchmark for learning-based monocular depth estimation methods. In recent years, depthmap calculation methods have achieved impressive results. The process of comparing the performance of different models requires a lot of resources and time, which can be costly for many developers. The goal of our benchmark is to provide a comparison of how the top models perform on various datasets. We present a table with results based on our tests performed on several popular datasets and one dataset introduced in this paper. In addition, we provide a toolkit that allows each model selected for the benchmark to be tested on any suitable dataset.

Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on. This task is challenging due to the need to adapt to various photo poses, the sensitivity of hairstyles, and the lack of objective metrics. The current state of the art hairstyle transfer methods use an optimization process for different parts of the approach, making them inexcusably slow. At the same time, faster encoder-based models are of very low quality because they either operate in StyleGAN's W+ space or use other low-dimensional image generators. Additionally, both approaches have a problem with hairstyle transfer when the source pose is very different from the target pose, because they either don't consider the pose at all or deal with it inefficiently. In our paper, we present the HairFast model, which uniquely solves these problems and achieves high resolution, near real-time performance, and superior reconstruction compared to optimization problem-based methods. Our solution includes a new architecture operating in the FS latent space of StyleGAN, an enhanced inpainting approach, and improved encoders for better alignment, color transfer, and a new encoder for post-processing. The effectiveness of our approach is demonstrated on realism metrics after random hairstyle transfer and reconstruction when the original hairstyle is transferred. In the most difficult scenario of transferring both shape and color of a hairstyle from different images, our method performs in less than a second on the Nvidia V100.

Abstract The processing of personal data gives a rise to many privacy concerns, and one of them is to ensure the transparency of data processing to end users. Usually this information is communicated to them using privacy policies. In this paper, the problem of user notification in case of data breaches and policy changes is addressed, besides an ontology-based approach to model them is proposed. To specify the ontology concepts and properties, the requirements and recommendations for the legislative regulations as well as existing privacy policies are evaluated. A set of SPARQL queries to validate the correctness and completeness of the proposed ontology are developed. The proposed approach is applied to evaluate the privacy policies designed by cloud computing providers and IoT device manufacturers. The results of the analysis show that the transparency of user notification scenarios presented in the privacy policies is still very low, and the companies should reconsider the notification mechanisms and provide more detailed information in privacy policies.

Nowadays the collection and usage of users' personal data have become an extremely common scenario. The users actively provide their personal data to customize or improve the quality of various digital services. Privacy policies are the only official way to inform data owners how their personal data are processed. There are different approaches for increasing the transparency of privacy policies and user agreements. This paper discusses ontology-based approaches and proposes formal descriptions of data processors' obligations relating to policy change and notification in case of a data breach.

Numerous online stock image libraries offer high quality yet copyright free images for use in marketing campaigns. To assist advertisers in navigating such third party libraries, we study the problem of automatically fetching relevant ad images given the ad text (via a short textual query for images). Motivated by our observations in logged data on ad image search queries (given ad text), we formulate a keyword extraction problem, where a keyword extracted from the ad text (or its augmented version) serves as the ad image query. In this context, we propose VisualTextRank: an unsupervised method to (i) augment input ad text using semantically similar ads, and (ii) extract the image query from the augmented ad text. VisualTextRank builds on prior work on graph based context extraction (biased TextRank in particular) by leveraging both the text and image of similar ads for better keyword extraction, and using advertiser category specific biasing with sentence-BERT embeddings. Using data collected from the Verizon Media Native (Yahoo Gemini) ad platform's stock image search feature for onboarding advertisers, we demonstrate the superiority of VisualTextRank compared to competitive keyword extraction baselines (including an 11% accuracy lift over biased TextRank). For the case when the stock image library is restricted to English queries, we show the effectiveness of VisualTextRank on multilingual ads (translated to English) while leveraging semantically similar English ads. Online tests with a simplified version of VisualTextRank led to a 28.7% increase in the usage of stock image search, and a 41.6% increase in the advertiser onboarding rate in the Verizon Media Native ad platform.

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled by organizing labels as a tree, like in the hierarchical softmax (HSM) approach commonly used for multi-class problems. In this paper, we investigate probabilistic label trees (PLTs) that have been recently devised for tackling XMLC problems. We show that PLTs are a no-regret multi-label generalization of HSM when precision@$k$ is used as a model evaluation metric. Critically, we prove that pick-one-label heuristic---a reduction technique from multi-label to multi-class that is routinely used along with HSM---is not consistent in general. We also show that our implementation of PLTs, referred to as extremeText (XT), obtains significantly better results than HSM with the pick-one-label heuristic and XML-CNN, a deep network specifically designed for XMLC problems. Moreover, XT is competitive to many state-of-the-art approaches in terms of statistical performance, model size and prediction time which makes it amenable to deploy in an online system.