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pro vyhledávání: '"Maier, Andreas"'
Emotions and smell are underrepresented in digital art history. In this exploratory work, we show that recognising emotions from smell-related artworks is technically feasible but has room for improvement. Using style transfer and hyperparameter opti
Externí odkaz:
http://arxiv.org/abs/2407.04592
Autor:
Weise, Tobias, Klumpp, Philipp, Demir, Kubilay Can, Pérez-Toro, Paula Andrea, Schuster, Maria, Noeth, Elmar, Heismann, Bjoern, Maier, Andreas, Yang, Seung Hee
This paper introduces a novel combination of two tasks, previously treated separately: acoustic-to-articulatory speech inversion (AAI) and phoneme-to-articulatory (PTA) motion estimation. We refer to this joint task as acoustic phoneme-to-articulator
Externí odkaz:
http://arxiv.org/abs/2407.03132
Autor:
Schneider, Linda-Sophie, Krauss, Patrick, Schiering, Nadine, Syben, Christopher, Schielein, Richard, Maier, Andreas
Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, informed by an understanding of the measurement process. These models gen
Externí odkaz:
http://arxiv.org/abs/2406.16659
In this paper, different techniques of few-shot, zero-shot, and regular object detection have been investigated. The need for few-shot learning and zero-shot learning techniques is crucial and arises from the limitations and challenges in traditional
Externí odkaz:
http://arxiv.org/abs/2406.16143
To develop intelligent speech assistants and integrate them seamlessly with intra-operative decision-support frameworks, accurate and efficient surgical phase recognition is a prerequisite. In this study, we propose a multimodal framework based on Ga
Externí odkaz:
http://arxiv.org/abs/2406.14576
Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions. Post-hoc explanation methods provide a way to understand neural netw
Externí odkaz:
http://arxiv.org/abs/2406.05477
Autor:
Thies, Mareike, Wagner, Fabian, Maul, Noah, Mei, Siyuan, Gu, Mingxuan, Pfaff, Laura, Vysotskaya, Nastassia, Yu, Haijun, Maier, Andreas
Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often inco
Externí odkaz:
http://arxiv.org/abs/2405.19079
The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature. Medical coding, a crucial process in healthcare, remains minimally
Externí odkaz:
http://arxiv.org/abs/2405.16115
Recent advances in computed tomography (CT) imaging, especially with dual-robot systems, have introduced new challenges for scan trajectory optimization. This paper presents a novel approach using Gated Recurrent Units (GRUs) to optimize CT scan traj
Externí odkaz:
http://arxiv.org/abs/2405.09333
Autor:
Krauss, Patrick, Hösch, Jannik, Metzner, Claus, Maier, Andreas, Uhrig, Peter, Schilling, Achim
The ability to transmit and receive complex information via language is unique to humans and is the basis of traditions, culture and versatile social interactions. Through the disruptive introduction of transformer based large language models (LLMs)
Externí odkaz:
http://arxiv.org/abs/2405.02024