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T.C. Markhorst
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2 records found
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In this paper, we jointly combine image classification and image denoising, aiming to enhance human perception of noisy images captured by edge devices, like low-light security cameras. In such settings, it is important to retain the ability of humans to verify the automatic classification decision and thus jointly denoise the image to enhance human perception. Since edge devices have little computational power, we explicitly optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural Architecture Search (NAS) method, which searches for classifiers to search for the integrated model while optimizing for a target latency, classification accuracy, and denoising performance. The NAS architectures outperform our manually designed alternatives in both denoising and classification, offering a significant improvement to human perception. Our approach empowers users to construct architectures tailored to domains like medical imaging, surveillance systems, and industrial inspections.
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In this paper, we jointly combine image classification and image denoising, aiming to enhance human perception of noisy images captured by edge devices, like low-light security cameras. In such settings, it is important to retain the ability of humans to verify the automatic classification decision and thus jointly denoise the image to enhance human perception. Since edge devices have little computational power, we explicitly optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural Architecture Search (NAS) method, which searches for classifiers to search for the integrated model while optimizing for a target latency, classification accuracy, and denoising performance. The NAS architectures outperform our manually designed alternatives in both denoising and classification, offering a significant improvement to human perception. Our approach empowers users to construct architectures tailored to domains like medical imaging, surveillance systems, and industrial inspections.
SpeechCAT
Cross-Attentive Transformer for Audio to Motion Generation
Conference paper
(2025)
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Sebastian Deaconu, Xiangwei Shi, Thomas Markhorst, Jouh Yeong Chew, Xucong Zhang
Audio-to-motion generation is an important task with applications in virtual avatar creation for XR systems and intelligent robot control in daily life scenarios. However, most existing motion generation methods rely on a single encoder-decoder architecture to model all body parts simultaneously, which limits their ability to capture the diverse and complex motions exhibited by humans. In this paper, we propose a novel method, SpeechCAT, that employs three separate encoder-decoder modules to individually model the motions of the face, body, and hands. To capture the relationships and synchronization among these body parts, we introduce a cross-attention mechanism to effectively learn their correlations. SpeechCAT ensures sufficient capacity to model the unique characteristics of each body part while preserving the coherence between them. Our experimental results demonstrate the superiority of SpeechCAT over baseline methods, highlighting its effectiveness in generating diverse, realistic, and synchronized motions with face, body, and hand parts.
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Audio-to-motion generation is an important task with applications in virtual avatar creation for XR systems and intelligent robot control in daily life scenarios. However, most existing motion generation methods rely on a single encoder-decoder architecture to model all body parts simultaneously, which limits their ability to capture the diverse and complex motions exhibited by humans. In this paper, we propose a novel method, SpeechCAT, that employs three separate encoder-decoder modules to individually model the motions of the face, body, and hands. To capture the relationships and synchronization among these body parts, we introduce a cross-attention mechanism to effectively learn their correlations. SpeechCAT ensures sufficient capacity to model the unique characteristics of each body part while preserving the coherence between them. Our experimental results demonstrate the superiority of SpeechCAT over baseline methods, highlighting its effectiveness in generating diverse, realistic, and synchronized motions with face, body, and hand parts.