no code implementations • 9 May 2024 • Zheming Zuo, Joseph Smith, Jonathan Stonehouse, Boguslaw Obara
Subsequently, a new dataset becomes available, prompting the desire to make a pivotal decision for achieving enhanced and leveraged inference performance on both sides: Should one opt to train datasets from scratch or fine-tune the model trained on the initial dataset using the newly released dataset?
no code implementations • 9 May 2024 • Joseph Smith, Zheming Zuo, Jonathan Stonehouse, Boguslaw Obara
In this paper, we propose a No-Reference Image Quality Assessment (NRIQA) guided cut-off point selection (CPS) strategy to enhance the performance of a fine-grained classification system.
no code implementations • 21 Sep 2022 • Han Xu, Zheming Zuo, Jie Li, Victor Chang
Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades.
1 code implementation • 10 Jan 2021 • Zheming Zuo, Jie Li, Han Xu, Noura Al Moubayed
Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques.