Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency

Head pose estimation plays a vital role in biometric systems related to facial and human behavior analysis. Typically, neural networks are trained on head pose datasets. Unfortunately, manual or sensor-based annotation of head pose is impractical. A solution is synthetic training data generated from 3D face models, which can provide an infinite number of perfect labels. However, computer generated images only provide an approximation of real-world images, leading to a performance gap between training and application domain. Therefore, there is a need for strategies that allow simultaneous learning on labeled synthetic data and unlabeled real-world data to overcome the domain gap. In this work we propose relative pose consistency, a semi-supervised learning strategy for head pose estimation based on consistency regularization. Consistency regularization enforces consistent network predictions under random image augmentations, including pose-preserving and pose-altering augmentations. We propose a strategy to exploit the relative pose introduced by pose-altering augmentations between augmented image pairs, to allow the network to benefit from relative pose labels during training on unlabeled data. We evaluate our approach in a domain-adaptation scenario and in a commonly used cross-dataset scenario. Furthermore, we reproduce related works to enforce consistent evaluation protocols and show that for both scenarios we outperform SOTA

Photon super-bunching from a generic tunnel junction

Generating correlated photon pairs at the nanoscale is a prerequisite to creating highly integrated optoelectronic circuits that perform quantum computing tasks based on heralded single-photons. Here we demonstrate fulfilling this requirement with a generic tip-surface metal junction. When the junction is luminescing under DC bias, inelastic tunneling events of single electrons produce a photon stream in the visible spectrum whose super-bunching index is 17 when measured with a 53 picosecond instrumental resolution limit. These photon bunches contain true photon pairs of plasmonic origin, distinct from accidental photon coincidences. The effect is electrically rather than optically driven - completely absent are pulsed lasers, down-conversions, and four-wave mixing schemes. This discovery has immediate and profound implications for quantum optics and cryptography, notwithstanding its fundamental importance to basic science and its ushering in of heralded photon experiments on the nanometer scale

AUC margin loss for limited, imbalanced and noisy medical image diagnosis - A case study on CheXpert5000

The AUC margin loss is a valuable loss function for medical image classification as it addresses the problems of imbalanced and noisy labels. It is used by the current winner of the CheXpert competition. The CheXpert dataset is a large dataset (200k+ images), however datasets in the range of 1k-10k medical datasets are much more common. This raises the question if optimizing AUC margin loss also is effective in scenarios with limited data.We compare AUC margin loss optimization to binary cross-entropy on limited, imbalanced and noisy CheXpert5000, a subset of CheXpert dataset. We show that AUC margin loss is beneficial for limited data and considerably improves accuracy in the presence of label noise. It also improves out-of-box calibration

Growth and surface alloying of Fe on Pt(997)

The growth of ultra-thin layers of Fe on the vicinal Pt(997) surface is studied by thermal energy He atom scattering (TEAS) and Auger electron spectroscopy (AES) in the temperature range between 175K and 800K. We find three distinct regimes of qualitatively different growth type: Below 450K the formation of a smooth first monolayer, at and above 600K the onset of bulk alloy formation, and at intermediate temperature 500K - 550K the formation of a surface alloy. Monatomic Fe rows are observed to decorate the substrate steps between 175K and 500K. The importance of the high step density is discussed with respect to the promotion of smooth layer growth and with respect to the alloying process and its kinetics

A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000

Computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets. Although these datasets are still small compared to natural image datasets like ImageNet1k, ImageNet21k and JFT, they are large for annotated medical datasets, where 1k-10k labeled samples are much more common. There is no baseline on which methods to build on in the low data regime. In this work we bridge this gap by providing an extensive study on medical image classification with limited annotations (5k). We present a study of modern architectures applied to a fixed low data regime of 5000 images on the CheXpert dataset. Conclusively we find that models pretrained on ImageNet21k achieve a higher AUC and larger models require less training steps. All models are quite well calibrated even though we only fine-tuned on 5000 training samples. All 'modern' architectures have higher AUC than ResNet50. Regularization of Big Transfer Models with MixUp or Mean Teacher improves calibration, MixUp also improves accuracy. Vision Transformer achieve comparable or on par results to Big Transfer Models.Comment: Accepted at MICCAI 202

Image-based Detection of Surface Defects in Concrete during Construction

Defects increase the cost and duration of construction projects. Automating defect detection would reduce documentation efforts that are necessary to decrease the risk of defects delaying construction projects. Since concrete is a widely used construction material, this work focuses on detecting honeycombs, a substantial defect in concrete structures that may even affect structural integrity. First, images were compared that were either scraped from the web or obtained from actual practice. The results demonstrate that web images represent just a selection of honeycombs and do not capture the complete variance. Second, Mask R-CNN and EfficientNet-B0 were trained for honeycomb detection to evaluate instance segmentation and patch-based classification, respectively achieving 47.7% precision and 34.2% recall as well as 68.5% precision and 55.7% recall. Although the performance of those models is not sufficient for completely automated defect detection, the models could be used for active learning integrated into defect documentation systems. In conclusion, CNNs can assist detecting honeycombs in concrete