no code implementations • 12 Dec 2023 • Tuan Truong, Farnaz Khun Jush, Matthias Lenga
Near- and duplicate image detection is a critical concern in the field of medical imaging.
no code implementations • 22 Nov 2023 • Farnaz Khun Jush, Tuan Truong, Steffen Vogler, Matthias Lenga
A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging.
no code implementations • 29 Sep 2023 • Tuan Truong, Hoang-Phi Nguyen, Tung Pham, Minh-Tuan Tran, Mehrtash Harandi, Dinh Phung, Trung Le
Motivated by this analysis, we introduce our algorithm, Riemannian Sharpness-Aware Minimization (RSAM).
1 code implementation • 20 May 2023 • Jia Qi Yip, Tuan Truong, Dianwen Ng, Chong Zhang, Yukun Ma, Trung Hieu Nguyen, Chongjia Ni, Shengkui Zhao, Eng Siong Chng, Bin Ma
In this paper, we propose ACA-Net, a lightweight, global context-aware speaker embedding extractor for Speaker Verification (SV) that improves upon existing work by using Asymmetric Cross Attention (ACA) to replace temporal pooling.
1 code implementation • 22 Nov 2022 • Chris Cameron, Jason Hartford, Taylor Lundy, Tuan Truong, Alan Milligan, Rex Chen, Kevin Leyton-Brown
We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in {tree MDPs}, for which policy execution involves traversing an exponential-sized tree.
no code implementations • 22 Apr 2022 • Tuan Truong, Matthias Lenga, Antoine Serrurier, Sadegh Mohammadi
Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an Area Under the Receiver Operating Characteristic Curve (AUC) score of 0. 8658, a sensitivity of 0. 8057, and a specificity of 0. 7958.
no code implementations • 23 Aug 2021 • Tuan Truong, Sadegh Mohammadi, Matthias Lenga
In addition, we introduce Dynamic Visual Meta-Embedding (DVME) as an end-to-end transfer learning approach that fuses pretrained embeddings from multiple models.