Zobrazeno 1 - 10
of 1 077
pro vyhledávání: '"Lau, Kin"'
Autor:
Lau, Kin Wai, Rehman, Yasar Abbas Ur, de Gusmão, Pedro Porto Buarque, Po, Lai-Man, Ma, Lan, Xie, Yuyang
Federated Learning (FL) has emerged as a privacy-preserving method for training machine learning models in a distributed manner on edge devices. However, on-device models face inherent computational power and memory limitations, potentially resulting
Externí odkaz:
http://arxiv.org/abs/2409.15898
Recent research has successfully adapted vision-based convolutional neural network (CNN) architectures for audio recognition tasks using Mel-Spectrograms. However, these CNNs have high computational costs and memory requirements, limiting their deplo
Externí odkaz:
http://arxiv.org/abs/2404.13551
The integration of Federated Learning (FL) and Self-supervised Learning (SSL) offers a unique and synergetic combination to exploit the audio data for general-purpose audio understanding, without compromising user data privacy. However, rare efforts
Externí odkaz:
http://arxiv.org/abs/2402.02889
Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional assumptions and thus suffer from the high dimensionality of latent fea
Externí odkaz:
http://arxiv.org/abs/2310.16587
Visual Attention Networks (VAN) with Large Kernel Attention (LKA) modules have been shown to provide remarkable performance, that surpasses Vision Transformers (ViTs), on a range of vision-based tasks. However, the depth-wise convolutional layer in t
Externí odkaz:
http://arxiv.org/abs/2309.01439
This report presents the technical details of our submission to the 2023 Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge. The task is to learn the mapping from audio samples to their corresponding action labels. To achieve this
Externí odkaz:
http://arxiv.org/abs/2307.07265
Autor:
Blanchett, Reid1 (AUTHOR), Lau, Kin H.2 (AUTHOR), Pfeifer, Gerd P.1 (AUTHOR) gerd.pfeifer@vai.org
Publikováno v:
Scientific Reports. 6/17/2024, Vol. 14 Issue 1, p1-14. 14p.
Autor:
Guak, Hannah, Weiland, Matthew, Ark, Alexandra Vander, Zhai, Lukai, Lau, Kin, Corrado, Mario, Davidson, Paula, Asiedu, Ebenezer, Mabvakure, Batsirai, Compton, Shelby, DeCamp, Lisa, Scullion, Catherine A., Jones, Russell G., Nowinski, Sara M., Krawczyk, Connie M.
Publikováno v:
In Cell Reports 27 August 2024 43(8)
Due to unreliable geometric matching and content misalignment, most conventional pose transfer algorithms fail to generate fine-trained person images. In this paper, we propose a novel framework Spatial Content Alignment GAN (SCAGAN) which aims to en
Externí odkaz:
http://arxiv.org/abs/2103.16828