Genetic Learning for Designing Sim-to-Real Data Augmentations

Data augmentations are useful in closing the sim-to-real domain gap when training on synthetic data. This is because they widen the training data distribution, thus encouraging the model to generalize better to other domains. Many image augmentation techniques exist, parametrized by different settings, such as strength and probability. This leads to a large space of different possible augmentation policies. Some policies work better than others for overcoming the sim-to-real gap for specific datasets, and it is unclear why. This paper presents two different interpretable metrics that can be combined to predict how well a certain augmentation policy will work for a specific sim-to-real setting, focusing on object detection. We validate our metrics by training many models with different augmentation policies and showing a strong correlation with performance on real data. Additionally, we introduce GeneticAugment, a genetic programming method that can leverage these metrics to automatically design an augmentation policy for a specific dataset without needing to train a model.Comment: 21 pages; accepted at DMLR Workshop @ ICRL 202

VATr++: Choose Your Words Wisely for Handwritten Text Generation

Styled Handwritten Text Generation (HTG) has received significant attention in recent years, propelled by the success of learning-based solutions employing GANs, Transformers, and, preliminarily, Diffusion Models. Despite this surge in interest, there remains a critical yet understudied aspect - the impact of the input, both visual and textual, on the HTG model training and its subsequent influence on performance. This study delves deeper into a cutting-edge Styled-HTG approach, proposing strategies for input preparation and training regularization that allow the model to achieve better performance and generalize better. These aspects are validated through extensive analysis on several different settings and datasets. Moreover, in this work, we go beyond performance optimization and address a significant hurdle in HTG research - the lack of a standardized evaluation protocol. In particular, we propose a standardization of the evaluation protocol for HTG and conduct a comprehensive benchmarking of existing approaches. By doing so, we aim to establish a foundation for fair and meaningful comparisons between HTG strategies, fostering progress in the field

CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.Comment: Accepted at the Workshop on Photorealistic Image and Environment Synthesis for Computer Vision (PIES-CV) at WACV2

Exploring the Gene Pool of Silver Fir in Southern Germany on the Search for Climate-Smart Seed Sources

Central European populations of silver fir (Abies alba Mill.) grow under a relatively wide amplitude of environmental conditions. Assuming that forest tree stands are locally adapted, the use of forest reproductive material from sites with limited water availability is expected to increase drought tolerance in future forests. At the same time, genetic diversity is of utmost importance as the basis of adaptation to a changing environment. Currently, a selection of potential sources for climate-smart reproductive material of silver fir is under way in Southern Germany. It is based on a multidisciplinary approach elucidating the genetic variation, site conditions, as well as tree resilience based on a dendroecological approach. The analysis encompasses a large number of stands representing the whole range of the species’ ecological requirements. The population genetic analysis based on molecular markers (nuclear microsatellites) provided important information concerning the gene pool of the species in Southern Germany. On one hand, it revealed genetic differentiation and transition zones between western and eastern clusters. On the other hand, the results indicated gradients and regional variation of genetic diversity. These patterns can be explained by post glacial recolonization and the peripheral character of the species at the northern limit of its distribution. Together with the outcomes of the site condition and dendroecological approaches, the results of the genetic analysis will be used to inform future provenance recommendations

Cent scientifiques répliquent à SEA (Suppression des Expériences sur l’Animal vivant) et dénoncent sa désinformation

La lutte contre la maltraitance animale est sans conteste une cause moralement juste. Mais elle ne justifie en rien la désinformation à laquelle certaines associations qui s’en réclament ont recours pour remettre en question l’usage de l’expérimentation animale en recherche

Plasma catalysis modeling : how ideal is atomic hydrogen for eley-rideal?

Abstract: Plasma catalysis is an emerging technology, but a lot of questions about the underlying surface mechanisms remain unanswered. One of these questions is how important Eley-Rideal (ER) reactions are, next to Langmuir-Hinshelwood reactions. Most plasma catalysis kinetic models predict ER reactions to be important and sometimes even vital for the surface chemistry. In this work, we take a critical look at how ER reactions involving H radicals are incorporated in kinetic models describing CO2 hydrogenation and NH3 synthesis. To this end, we construct potential energy surface (PES) intersections, similar to elbow plots constructed for dissociative chemisorption. The results of the PES intersections are in agreement with ab initio molecular dynamics (AIMD) findings in literature while being computationally much cheaper. We find that, for the reactions studied here, adsorption is more probable than a reaction via the hot atom (HA) mechanism, which in turn is more probable than a reaction via the ER mechanism. We also conclude that kinetic models of plasma-catalytic systems tend to overestimate the importance of ER reactions. Furthermore, as opposed to what is often assumed in kinetic models, the choice of catalyst will influence the ER reaction probability. Overall, the description of ER reactions is too much "ideal" in models. Based on our findings, we make a number of recommendations on how to incorporate ER reactions in kinetic models to avoid overestimation of their importance

Sim-to-Real Dataset of Industrial Metal Objects

We present a diverse dataset of industrial metal objects with unique characteristics such as symmetry, texturelessness, and high reflectiveness. These features introduce challenging conditions that are not captured in existing datasets. Our dataset comprises both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data were obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions, and lighting conditions. This resulted in over 30,000 real-world images. We introduce a new public tool that enables the quick annotation of 6D object pose labels in multi-view images. This tool was used to provide 6D object pose labels for all real-world images. Synthetic data were generated by carefully simulating real-world conditions and varying them in a controlled and realistic way. This resulted in over 500,000 synthetic images. The close correspondence between synthetic and real-world data and controlled variations will facilitate sim-to-real research. Our focus on industrial conditions and objects will facilitate research on computer vision tasks, such as 6D object pose estimation, which are relevant for many industrial applications, such as machine tending. The dataset and accompanying resources are available on the project website

Improving Molecule-Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO2 Hydrogenation

Density functional theory (DFT) is widely used to gain insight in molecule-metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well known that generalized gradient approximation (GGA) density functionals (DF), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the self-interaction energy. Screened hybrid GGA DFs have been shown to reduce this problem, but are currently intractable for wide usage. In this work we use a more affordable meta-generalized gradient approximation (mGGA) DF in combination with a non-local correlation DF for the first time to study a catalytically important surface reaction network, namely CO2 hydrogenation on Cu. We show that the mGGA DF used, namely rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals, molecules, as well as molecule-metal surface adsorption and activation energies. Hence, it is a better choice for constructing molecule-metal surface reaction networks