Andreas Dengel

Deutsches Forschungszentrum für Künstliche Intelligenz | DFKI · Smart Data & Knowledge Services

With an aim to eliminate or reduce the spread of hate content across social media platforms, the development of artificial intelligence supported computational predictors is an active area of research. However, diversity of languages hinders development of generic predictors that can precisely identify hate content. Several language-specific hate s...

Model interpretability and robustness are becoming increasingly critical today for the safe and practical deployment of deep learning (DL) models in industrial settings. As DL-backed automated document processing systems become increasingly common in business workflows, there is a pressing need today to enhance interpretability and robustness for t...

As data-driven AI systems become increasingly integrated into industry, concerns have recently arisen regarding potential privacy breaches and the inadvertent leakage of sensitive user data through the exploitation of these systems. In this paper, we explore the intersection of data privacy and AI-powered document analysis systems, presenting a com...

p>Deep neural networks have demonstrated exceptional performance breakthroughs in the field of document image classification; yet, there has been limited research in the field that delves into the explainability of these models. In this paper, we present a comprehensive study in which we analyze 9 different explainability methods across 10 differen...

p>Model interpretability and robustness are becoming increasingly critical today for the safe and practical deployment of deep learning (DL) models in industrial settings. As DL-backed automated document processing systems become increasingly common in business workflows, there is a pressing need today to enhance interpretability and robustness for...

p>Deep neural networks have demonstrated exceptional performance breakthroughs in the field of document image classification; yet, there has been limited research in the field that delves into the explainability of these models. In this paper, we present a comprehensive study in which we analyze 9 different explainability methods across 10 differen...

Knowledge Transfer is one of the essential principles in education. The teacher’s knowledge is encoded through speech and writing and transmitted to the student. The student then decodes the transmitted information according to individual capabilities and absorbs it as knowledge. This paper presents an approach to accelerate knowledge transfer usin...

YY1-mediated chromatin loops play substantial roles in basic biological processes like gene regulation, cell differentiation, and DNA replication. YY1-mediated chromatin loop prediction is important to understand diverse types of biological processes which may lead to the development of new therapeutics for neurological disorders and cancers. Exist...

Long Short-Term Memory (LSTM) typically relies solely on historical data for training and although, they excel at modelling sequential series and finding hidden patterns in the data, they are unable to utilize expert knowledge. Knowledge-driven systems (KDS), on the other hand, rely on domain knowledge and consist of rules explicitly defined by hum...

This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR). DWA invigorates an approach recently receiving less attention, namely Discrete Wavelet Transformation (DWT). DWT enables an efficient image representation for SR and reduces the spatial area of its input by a factor of 4, the o...

Cells are essential to life because they provide the functional, genetic, and communication mechanisms essential for the proper functioning of living organisms. Cell segmentation is pivotal for any biological hypothesis validation/analysis i.e., to get valuable insights into cell behavior, function, diagnosis, and treatment. Deep learning-based seg...

Gaze estimation is an important factor in human activity and behavior recognition. The technology is used in numerous applications such as human-computer interaction, driver monitoring systems, and surveillance. Gaze estimation can be achieved using different technologies such as wearable devices or cameras. Estimating gaze using a webcam can indee...

Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding thes...

YY1-mediated chromatin loops play substantial roles in basic biological processes like gene regulation, cell differentiation, and DNA replication. YY1-mediated chromatin loop prediction is important to understand diverse types of biological processes which may lead to the development of new therapeutics for neurological disorders and cancers. Exist...

Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding thes...

Cell microscopy is the main tool that allows researchers to study microorganisms and plays a key role in observing and understanding the morphology, interactions, and development of microorganisms. However, there exist limitations in both the techniques and the samples that impair the amount of available data to study. Generative adversarial networ...

Exponential growth of electronic data requires advanced multi-label classification approaches for the development of natural language processing (NLP) applications such as recommendation systems, drug reaction detection, hate speech detection and opinion recognition/mining. To date, several machine and deep learning based multi-label classification...

Intelligence Augmentation (IA) has long been understood as a concept that describes how human capabilities are enhanced by technologies to improve their intelligence (often in a collective sense), and therefore improve the outcomes of many tasks. While IA’s goal is to keep the human in the loop and design technology around users, the future of IA a...

We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data. Our method is based on Segment Anything, a vision foundation model for image segmentation. We extend it by training specialized models for microscopy data that significantly improve segmentation...

Document images, when captured in real-world settings, either modern or historical, frequently exhibit various forms of degradation such as ink stains, smudges, faded text, and uneven illumination, which can significantly impede the performance of deep learning-based approaches for document processing. In this paper, we propose a novel end-to-end f...

The growing popularity of Vision Transformers as the go-to models for image classification has led to an explosion of architectural modifications claiming to be more efficient than the original ViT. However, a wide diversity of experimental conditions prevents a fair comparison between all of them, based solely on their reported results. To address...

We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and machine learning model agnostic methods at the sub-field level. We use Sentinel-2 satellite imagery as...

Paper-based handwritten electrical circuit diagrams still exist in educational scenarios and historical contexts. In order to check them or to derive their functional principles, they can be digitized for further analysis and simulation. This digitization effectively performs an electrical graph extraction and can be achieved by straight-forward in...

This work introduces "You Only Diffuse Areas" (YODA), a novel method for partial diffusion in Single-Image Super-Resolution (SISR). The core idea is to utilize diffusion selectively on spatial regions based on attention maps derived from the low-resolution image and the current time step in the diffusion process. This time-dependent targeting enabl...

With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitude, and noise of RS data. The typical approach for merging multiple RS sources has been input-level fusion, but other - more advanc...

This paper presents a retrospective overview of a decade of research in our department towards self-organizing personal knowledge assistants in evolving corporate memories. Our research is typically inspired by real-world problems and often conducted in interdisciplinary collaborations with research and industry partners. We summarize past experime...

Graph neural networks (GNNs) have found majority of applications in multiple domains including physical science, molecular biology, etc. However, there is still scope of research in the application of GNNs in electrical domain. This research work tries to generate the circuit graphs that analyzes the GNNs. The end goal is to create a mechanism to i...

Cells are essential to life because they provide the functional, genetic, and communication mechanisms essential for the proper functioning of living organisms. Cell segmentation is pivotal for any biological hypothesis validation/analysis i.e., to get valuable insights into cell behavior , function, diagnosis, and treatment. Deep learning-based se...

Despite recent breakthroughs in the domain of implicit generative models, the task of evaluating these models remains a challenging task. With no single metric to assess overall performance, various existing metrics only offer partial information. This issue is further compounded for unintuitive data types such as time series, where manual inspecti...

This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR). DWA invigorates an approach recently receiving less attention, namely Discrete Wavelet Transformation (DWT). DWT enables an efficient image representation for SR and reduces the spatial area of its input by a factor of 4, the o...

Artificial Intelligence (AI) has achieved remarkable success in image generation, image analysis, and language modeling, making data-driven techniques increasingly relevant in practical real-world applications, promising enhanced creativity and efficiency for human users. However, the deployment of AI in high-stakes domains such as infrastructure a...

Traditionally, spherical keypoint matching has been performed using greedy algorithms, such as Nearest Neighbors (NN) search. NN based algorithms often lead to erroneous or insufficient matches as they fail to leverage global keypoint neighborhood information. Inspired by a recent learned perspective matching approach [53] we introduce SphereGlue:...

Autonomous driving systems rely heavily on the underlying perception module which needs to be both performant and efficient to allow precise decisions in real-time. Avoiding collisions with pedestrians is of topmost priority in any autonomous driving system. Therefore, pedestrian detection is one of the core parts of such systems' perception module...

Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, a...

In the current digital era, it is remarkably convenient for researchers to share and collaborate on novel scientific ideas. Scientists aim to accomplish these endeavors through closely knitted scientific communities, depending on the domain. Technological advancements and their evolution overtime gave rise to a boom in the emergence of research com...

Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well known, however, that...

In the main text, we complemented previous surveys by critically identifying current strategies and new research areas. The supplementary material hereby gives further information and visualizations on the topics discussed. It supports understanding the main concepts and ideas examined in the main text.

This paper presents a novel Diffusion-Wavelet (DiWa) approach for Single-Image Super-Resolution (SISR). It leverages the strengths of Denoising Diffusion Probabilistic Models (DDPMs) and Discrete Wavelet Transformation (DWT). By enabling DDPMs to operate in the DWT domain, our DDPM models effectively hallucinate high-frequency information for super...

Deep learning has proven to be successful in various domains and for different tasks. However, when it comes to private data several restrictions are making it difficult to use deep learning approaches in these application fields. Recent approaches try to generate data privately instead of applying a privacy-preserving mechanism directly, on top of...

Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area affected by a flood event to emergency-relief units in near-real-time. Building upon the recent development of t...

Post-translational modifications (PTMs) either enhance a protein activity in various sub-cellular processes, or degrade their activity which leads towards failure of intracellular processes. Tyrosine nitration (NT) modification degrades proteins activity that initiate and propagate various diseases including Neurodegenerative, Cardiovascular, Autoi...

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research, e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent adv...

Video Object Segmentation is a fundamental task in computer vision that aims at pixel-wise tracking of one or multiple foreground objects within a video sequence. This task is challenging due to real-world requirements such as handling unconstrained object and camera motion, occlusion, fast motion, and motion blur. Recently, methods utilizing RNNs...

Despite various breakthroughs in machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obstruct this progress. Among these shortcomings, the absence of freely available data due to data privacy or high costs of data gathering and the nonexistence of adequate r...

Handwritten circuit diagrams from educational scenarios or historic sources usually exist on analogue media. For deriving their functional principles or flaws automatically, they need to be digitized, extracting their electrical graph. Recently, the base technologies for automated pipelines facilitating this process shifted from computer vision to...

Nucleosomes are complexes of histone and DNA base pairs in which DNA is wrapped around histone proteins to achieve compactness. Nucleosome positioning is associated with various biological processes such as DNA replication, gene regulation, DNA repair, and its dysregulation can lead to various diseases such as sepsis, and tumor. Since nucleosome po...

Measuring the level of engagement among participants in a meeting is crucial for evaluating collective understanding. While previous studies have utilized multiple sensors, such as wearable devices, to gauge engagement levels in offline environments, the shift to remote meetings due to the COVID-19 pandemic presents new challenges. In this study, w...

The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of information. They might contain static or temporary views with different levels of resolution, in addition to having different types and amounts of noise due to sensor calibr...

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable...

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range...

Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well-known, however, that...

Since the mid-10s, the era of Deep Learning (DL) has continued to this day, bringing forth new superlatives and innovations each year. Nevertheless, the speed with which these innovations translate into real applications lags behind this fast pace. Safety-critical applications, in particular, underlie strict regulatory and ethical requirements whic...

The increasing complexity of low‐voltage networks poses a growing challenge for the reliable and fail‐safe operation of electricity grids. The reasons for this include an increasingly decentralised energy generation (photovoltaic systems, wind power etc.) and the emergence of new types of consumers (e‐mobility, domestic electricity storage etc.). A...

Generative models are designed to address the data scarcity problem. Even with the exploding amount of data, due to computational advancements, some applications (e.g., health care, weather forecast, fault detection) still suffer from data insufficiency, especially in the time-series domain. Thus generative models are essential and powerful tools,...

p> Convolutional Neural Networks (ConvNets) have been thoroughly researched for document image classification and are known for their exceptional performance in unimodal image-based document classification. Recently, however, there has been a sudden shift in the field towards multimodal approaches that simultaneously learn from the visual and textu...