Massive, multi-language, monolithic repositories form the backbone of many modern, complex software systems. To ensure consistent code quality while still allowing fast development cycles, Continuous Integration (CI) is commonly applied. However, operating CI at such scale not only leads to a single point of failure for many developers, but also requires computational resources that may reach feasibility limits and cause long feedback latencies. To address these issues, developers commonly split test executions across multiple pipelines, running small and fast tests in pre-submit stages while executing long-running and flaky tests in post-submit pipelines. Given the long runtimes of many pipelines and the substantial proportion of passing test executions (98 % in our pre-submit pipelines), there not only a need but also potential for further improvements by prioritizing and selecting tests. However, many previously proposed regression optimization techniques are unfit for an industrial context, because they (1) rely on complex and difficult-to-obtain features like per-test code coverage that are not feasible in large, multi-language environments, (2) do not automatically adapt to rapidly changing systems where new tests are continuously added or modified, and (3) are not designed to distinguish the different objectives of pre- and post-submit pipelines: While pre-submit testing should prioritize failing tests, post-submit pipelines should prioritize tests that indicate non-flaky changes by transitioning from pass to fail outcomes or vice versa. To overcome these issues, we developed a lightweight and pipeline-aware regression test optimization approach that employs Reinforcement Learning models trained on language-agnostic features. We evaluated our approach on a large industry dataset collected over a span of 20 weeks of CI test executions. When predicting the failure likelihood in pre-submit pipelines, our approach scheduled the first failing test within the first 16 % of tests, outperforming existing approaches. When predicting test transitions in the post-submit pipeline, it was able to select 87 % of developer-relevant tests by cutting the test execution time in half and over 99 % within five cycles.

While fully-convolutional neural networks are very strong at modeling local features, they fail to aggregate global context due to their constrained receptive field. Modern methods typically address the lack of global context by introducing cascades, pooling, or by fitting a statistical model. In this work, we propose a new approach that introduces global context into a fully-convolutional neural network directly. The key concept is an implicit kernel convolution within the network. The kernel convolution blurs the output of a local-context subnet, which is then refined by a global-context subnet using dilated convolutions. The kernel convolution is crucial for the convergence of the network because it smoothens the gradients and reduces overfitting. In a postprocessing step, a simple PCA-based 2D shape model is fitted to the network output in order to filter outliers. Our experiments demonstrate the effectiveness of our approach, outperforming several state-of-the-art methods in facial landmark detection.

Despite the growing research interest in 2.5D and 3D video face processing, 3D facial videos are actually scarcely available. This work introduces a new open source tool, named FaceGrabber, for capturing human faces using Microsoft's Kinect 2.0. FaceGrabber permits the concurrent recording of various formats, including the raw 2D and 2.5D video streams, 3D point clouds and the 3D registered face model provided by the Kinect. The software is also able to convert different data formats and playback recorded results directly in 3D. In order to encourage research with Kinect 2.0 face data, we publish a new public video face database which was captured using FaceGrabber. The database comprises 40 individuals, performing the six universal emotions (disgust, sadness, happiness, fear, anger, surprise) and two additional sequences.

Visual attributes are high-level semantic descriptions of visual data that are close to the human language. They have been used intensively in various applications such as image classification, active learning, and interactive search. However, the usage of attributes in subspace learning (or dimensionality reduction) has not been considered yet. In this work, we propose to utilize relative attributes as semantic cues in subspace learning. To this end, we employ Non-negative Matrix Factorization (NMF) constrained by embedded relative attributes to learn a subspace representation of image content. Experiments conducted on two datasets show the efficiency of attributes in discriminative subspace learning.

Image inpainting algorithms aim to cut out parts of the image without leaving holes. Various algorithms exist, but no wider comparison has been made, yet. This work fills the gap by comparing state-of-the-art algorithms in a user study. We create and publish a database consisting of multiple base images and inpaint them using different inpainting concepts. Afterwards, 21 participants are asked to rate the quality of these inpainted images. The subjective feedback indicates that different image inpainting algorithms are favorable depending on the characteristics of the base image and target region. Furthermore, the results show that general image quality measures such as the peak signal-to-noise ratio (PSNR) or the structural similarity (SSIM) index are not suited for judging inpainting quality.

Feature learning algorithms aim to provide a compact and discriminative representation of complex datasets in order to increase the speed and accuracy of clustering or classification. In this paper, we propose a novel interactive feature learning approach which is mainly based on 3D interactive data visualization and Non-negative Matrix Factorization (NMF). Here, the data is visualized in a 3D interface to support human-data interaction. The user interactions are exploited in an NMF framework to learn a compact representation of the data. The conducted experiments on Synthetic Aperture Radar (SAR) images confirm the efficiency of the proposed approach.

In the area of foreground and background segmentation the dynamics of a scene often influence the model of the background. There exist different methods trying to overcome the difficulty in estimating and handling these dynamics. In this paper, we propose an adaption of proportional-integral-derivative (PID) controllers to this regulation problem. In our approach, PID controllers regulate the decision threshold of the background dynamics and the update rate of the background model. The regulators are integrated in the Pixel-based Adaptive Segmenter (PBAS), which is publicly available and offers good results in the Change Detection challenge. We show that the performance of the PBAS can be further improved through an empirical tuning of the single PID constants.

Mobile touch PCs allow interactions with virtual objects in augmented reality scenes. Manipulations of 3D objects are a common way of such interactions, which can be performed in three different coordinate systems: the camera-, object- and world coordinate systems. The camera coordinate system changes continuously in augmented reality as it depends on the mobile device's pose. The axis orientations of the world coordinate system are steady, whereas the axes of the object coordinates base on previous manipulations. The selection of a coordinate system therefore influences the 3D transformation's orientation independent from the used manipulation type.

This paper presents an AR videoconferencing approach merging two remote rooms into a shared workspace. Such bilateral AR telepresence inherently suffers from breaks in immersion stemming from the different physical layouts of participating spaces. As a remedy, we develop an automatic alignment scheme which ensures that participants share a maximum of common features in their physical surroundings. The system optimizes alignment with regard to initial user position, free shared floor space, camera positioning and other factors. Thus we can reduce discrepancies between different room and furniture layouts without actually modifying the rooms themselves. A description and discussion of our alignment scheme is given along with an exemplary implementation on real-world datasets.