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.

Image resolution, or in general, image quality, plays an essential role in the performance of today's face recognition systems. To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models. With octuplet loss, we leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification on various datasets without meaningfully exacerbating the performance on high-to-high resolution images. Our method applied on the FaceTransformer network achieves 95.12% face verification accuracy on the challenging XQLFW dataset while reaching 99.73% on the LFW database. Moreover, the low-to-low face verification accuracy benefits from our method. We release our code11Code available on https://github.com/Martlgap/octuplet-loss to allow seamless integration of the octuplet loss into existing frameworks.

In the area of face completion, the missing information within an occluded area is estimated, yielding a realistic face of the same identity. In most previous works, the mask describing the occluded region is known, limiting the scope of application. To alleviate this limitation, we propose a coarse-to-fine network trained as a conditional generative adversarial network. While the coarse network predicts the mask and generates a rough estimation of the semantic content, the subsequent fine network refines the rough prediction into a realistic and identity-persevering reconstruction. This is achieved by incorporating adversarial loss and using features from a pretrained face feature extractor. Unlike previous approaches, we employ two parallel attention mechanisms: 1) a patchwise cross-attention module to substitute information within the occluded patches with patches from the non-occluded region; 2) a pixel-wise global self-attention to allow information exchange within the entire feature map. Our exhaustive analysis, including reconstruction quality and face recognition metrics, shows that our approach outperforms the state of the art in blind face completion, improving the true positive identification rate at rank 1 on the MegaFace benchmark from 36.55 % to 42.48 %. This represents a substantial step towards closing the gap between occluded (29.34 %) and non-occluded faces (52.32 %). In terms of reconstruction quality, we obtain a structural similarity of 0.9639 compared to 0.8526 and 0.9563 for occluded faces and the state of the art, respectively. In addition to previous approaches, we provide an in-depth analysis of the influence of the position, size, and sparsity of the occlusion and use facial landmark prediction to measure reconstruction quality.

Real-world face recognition applications often deal with suboptimal image quality or resolution due to different capturing conditions such as various subject-to-camera distances, poor camera settings, or motion blur. This characteristic has an unignorable effect on performance. Recent cross-resolution face recognition approaches used simple, arbitrary, and unrealistic down- and up-scaling techniques to measure robustness against real-world edge-cases in image quality. Thus, we propose a new standardized benchmark dataset and evaluation protocol derived from the famous Labeled Faces in the Wild (LFW). In contrast to previous derivatives, which focus on pose, age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in the Wild (XQLFW) maximizes the quality difference. It contains only more realistic synthetically degraded images when necessary. Our proposed dataset is then used to further investigate the influence of image quality on several state-of-the-art approaches. With XQLFW, we show that these models perform differently in cross-quality cases, and hence, the generalizing capability is not accurately predicted by their performance on LFW. Additionally, we report baseline accuracy with recent deep learning models explicitly trained for cross-resolution applications and evaluate the susceptibility to image quality. To encourage further research in cross-resolution face recognition and incite the assessment of image quality robustness, we publish the database and code for evaluation.11Code, dataset and evaluation protocol available on https://martlgap.github.io/xqlfw

Typical approaches for video face recognition aggregate faces in a feature space to obtain a single feature representing the entire video. Unlike most previous approaches, we aggregate the faces directly in order to additionally obtain a single representative face as an intermediate output, from which a more discriminative feature vector is extracted. To overcome the limitation of a fixed number of input images of the state of the art in face aggregation, we incorporate a permutation invariant U-Net architecture capable of processing an arbitrary number of frames, which is employed in a generative adversarial network. We demonstrate the effectiveness of our method on three popular benchmark datasets for video face recognition. Our approach outperforms the baselines on the YouTube Faces dataset, obtaining an accuracy of 96.62%. Besides, we show that our method is robust against motion blur.

Photos of faces captured in unconstrained environments, such as large crowds, still constitute challenges for current face recognition approaches as often faces are occluded by objects or people in the foreground. However, few studies have addressed the task of recognizing partial faces. In this paper, we propose a novel approach to partial face recognition capable of recognizing faces with different occluded areas. We achieve this by combining attentional pooling of a ResNet’s intermediate feature maps with a separate aggregation module. We further adapt common losses to partial faces in order to ensure that the attention maps are diverse and handle occluded parts. Our thorough analysis demonstrates that we outperform all baselines under multiple benchmark protocols, including naturally and synthetically occluded partial faces. This suggests that our method successfully focuses on the relevant parts of the occluded face.

Approaches for kinship verification often rely on cosine distances between face identification features. However, due to gender bias inherent in these features, it is hard to reliably predict whether two opposite-gender pairs are related. Instead of fine tuning the feature extractor network on kinship verification, we propose a comparator network to cope with this bias. After concatenating both features, cascaded local expert networks extract the information most relevant for their corresponding kinship relation. We demonstrate that our framework is robust against this gender bias and achieves comparable results on two tracks of the RFIW Challenge 2020. Moreover, we show how our framework can be further extended to handle partially known or unknown kinship relations.

In the domain of audio-visual person recognition, many approaches use naive fusion techniques, such as scorelevel fusion or concatenation, to fuse the features obtained by face and audio extraction networks. More sophisticated methods fuse both features taking into account the quality of their corresponding inputs. In this paper, we propose a novel architecture to improve the prediction of feature quality. In contrary to previous works, which estimate feature quality based on the features themselves, we combine the information obtained from different layers of the feature extraction networks. In our analysis, we show that our approach outperforms state-of-the-art fusion approaches on well-established benchmarks for multimodal person verification. Moreover, we show that our model is robust against degradation of the visual input.

Current approaches for video face recognition rely on image sets containing faces of exclusively one identity. However, as image sets are created by unsupervised methods, it is necessary to consider outlier-afflicted sets for real-life applications. In this paper, we propose an Outlier-Robust Neural Aggregation Network (ORNAN). First, we embed each image into a feature space using a Convolutional Neural Network (CNN). With the help of two cascaded attention blocks, we predict outliers within the image set. By integrating this knowledge into our aggregation network, we adaptively aggregate all feature vectors to form a single feature, mitigating the influence of outliers and noisy features. We show that our network is robust against outliers using outlier-afflicted IJB-B and IJB-C benchmarks while maintaining similar performance without outliers.

Currently, self-localization of vehicles is primarily carried out by GPS receivers of different price and performance classes, which are permanently installed in vehicles. Known limitations of GPS-based localization include signal disturbances due to multipath propagation, as it may occur in narrow street canyons or tree alleys, as well as signal losses in tunnels or entrances to buildings, for example. It may happen that horizontal GPS channels still support a useful 2D positioning with low uncertainties while the GPS altitude channel has very large deviations or error uncertainties. Additional sensors (IMU or cameras) help to detect sections of trajectories where the GPS signal becomes unusable. Based on a traditional navigation strategy (i.e. integration of IMU and GPS), we also study the possible integration of visual odometry obtained from a stereo camera system. The paper reports about a large-scale project studying multi-sensor integration for very accurate and robust self-localization of vehicles.