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pro vyhledávání: '"Schmid, Ute"'
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and se
Externí odkaz:
http://arxiv.org/abs/2408.06190
Autor:
Bahr, Lukas, Wehner, Christoph, Wewerka, Judith, Bittencourt, José, Schmid, Ute, Daub, Rüdiger
Failure mode and effects analysis (FMEA) is a critical tool for mitigating potential failures, particular during ramp-up phases of new products. However, its effectiveness is often limited by the missing reasoning capabilities of the FMEA tools, whic
Externí odkaz:
http://arxiv.org/abs/2406.18114
Recent advancements in generative AI have introduced novel prospects and practical implementations. Especially diffusion models show their strength in generating diverse and, at the same time, realistic features, positioning them well for generating
Externí odkaz:
http://arxiv.org/abs/2406.01649
Ensuring the quality of black-box Deep Neural Networks (DNNs) has become ever more significant, especially in safety-critical domains such as automated driving. While global concept encodings generally enable a user to test a model for a specific con
Externí odkaz:
http://arxiv.org/abs/2405.17523
Explanations for Convolutional Neural Networks (CNNs) based on relevance of input pixels might be too unspecific to evaluate which and how input features impact model decisions. Especially in complex real-world domains like biology, the presence of s
Externí odkaz:
http://arxiv.org/abs/2405.01661
The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples of the desired behaviour of the program. A key underlying assumption is that humans can provide examples of sufficient quality to
Externí odkaz:
http://arxiv.org/abs/2404.19397
Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since
Externí odkaz:
http://arxiv.org/abs/2404.03499
Explaining concepts by contrasting examples is an efficient and convenient way of giving insights into the reasons behind a classification decision. This is of particular interest in decision-critical domains, such as medical diagnostics. One particu
Externí odkaz:
http://arxiv.org/abs/2308.14163