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Multi-faceted Deep Learning
This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the research...
Computer Vision – ECCV 2020 Workshops
The 6-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic. The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics. Part I focusses on adversarial robustness in the real world; bioimage computation; egocentric perception, interaction and computing; eye gaze in VR, AR, and in the wild; TASK-CV workshop and VisDA challenge; and bodily expressed emotion understanding.
Explainable and Interpretable Models in Computer Vision and Machine Learning
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning m...
Pattern Recognition
This book constitutes the proceedings of the 45th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2023, which took place in Heidelberg, Germany, during September 19-22, 2023. The 40 full papers included in these proceedings were carefully reviewed and selected from 76 submissions. They were organized in topical sections as follows: Segmentation and action recognition; 3D reconstruction and neural rendering; Photogrammetry and remote sensing; Pattern recognition in the life sciences; Interpretable machine learning; Weak supervision and online learning; Robust models.
At the Edge of AI
How are human computation systems developed in the field of citizen science to achieve what neither humans nor computers can do alone? Through multiple perspectives and methods, Libuse Hannah Veprek examines the imagination of these assemblages, their creation, and everyday negotiation in the interplay of various actors and play/science entanglements at the edge of AI. Focusing on their human-technology relations, this ethnographic study shows how these formations are marked by intraversions, as they change with technological advancements and the actors' goals, motivations, and practices. This work contributes to the constructive and critical ethnographic engagement with human-AI assemblages in the making.
xxAI - Beyond Explainable AI
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towar...
Pattern Recognition
This book constitutes the refereed proceedings of the 42nd German Conference on Pattern Recognition, DAGM GCPR 2020, which took place during September 28 until October 1, 2020. The conference was planned to take place in Tübingen, Germany, but had to change to an online format due to the COVID-19 pandemic. The 34 papers presented in this volume were carefully reviewed and selected from a total of 89 submissions. They were organized in topical sections named: Normalizing Flow, Semantics, Physics, Camera Calibration and Computer Vision, Pattern Recognition, Machine Learning.
Contributions to Large-scale Learning for Image Classification
Building algorithms that classify images on a large scale is an essential task due to the difficulty in searching massive amount of unlabeled visual data available on the Internet. We aim at classifying images based on their content to simplify the manageability of such large-scale collections. Large-scale image classification is a difficult problem as datasets are large with respect to both the number of images and the number of classes. Some of these classes are fine grained and they may not contain any labeled representatives. In this thesis, we use state-of-the-art image representations and focus on efficient learning methods. Our contributions are (1) a benchmark of learning algorithms ...
Pattern Recognition
This book constitutes the refereed proceedings of the 42nd German Conference on Pattern Recognition, DAGM GCPR 2020, which took place during September 28 until October 1, 2020. The conference was planned to take place in Tübingen, Germany, but had to change to an online format due to the COVID-19 pandemic. The 34 papers presented in this volume were carefully reviewed and selected from a total of 89 submissions. They were organized in topical sections named: Normalizing Flow, Semantics, Physics, Camera Calibration and Computer Vision, Pattern Recognition, Machine Learning.
