no code implementations • LREC 2022 • Emma Barker, Jon Barker, Robert Gaizauskas, Ning Ma, Monica Lestari Paramita
We present SNuC, the first published corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector.
no code implementations • 21 Feb 2024 • Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu
Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language.
1 code implementation • ICCV 2023 • Ke Liu, Feng Liu, Haishuai Wang, Ning Ma, Jiajun Bu, Bo Han
Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs.
no code implementations • 20 Oct 2023 • Zehai Tu, Ning Ma, Jon Barker
This paper describes two intelligibility prediction systems derived from a pretrained noise-robust automatic speech recognition (ASR) model for the second Clarity Prediction Challenge (CPC2).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 10 Aug 2023 • Ming Gu, Gaoming Yang, Sheng Zhou, Ning Ma, Jiawei Chen, Qiaoyu Tan, Meihan Liu, Jiajun Bu
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.
no code implementations • 30 May 2023 • Yifu Zhang, Hongru Li, Tao Yang, Rui Tao, Zhengyuan Liu, Shimeng Shi, Jiansong Zhang, Ning Ma, Wujin Feng, Zhanhu Zhang, Xinyu Zhang
Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain.
no code implementations • 8 Apr 2022 • Zehai Tu, Jack Deadman, Ning Ma, Jon Barker
End-to-end models have achieved significant improvement on automatic speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 8 Apr 2022 • Zehai Tu, Ning Ma, Jon Barker
An accurate objective speech intelligibility prediction algorithms is of great interest for many applications such as speech enhancement for hearing aids.
1 code implementation • 8 Apr 2022 • Zehai Tu, Ning Ma, Jon Barker
Non-intrusive intelligibility prediction is important for its application in realistic scenarios, where a clean reference signal is difficult to access.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 14 Jul 2021 • Ning Ma, Jiajun Bu, Lixian Lu, Jun Wen, Zhen Zhang, Sheng Zhou, Xifeng Yan
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.
no code implementations • 14 Jul 2021 • Ning Ma, Jiajun Bu, Zhen Zhang, Sheng Zhou
Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data.
no code implementations • 15 Mar 2021 • Zehai Tu, Ning Ma, Jon Barker
In this paper, we explore an alternative approach to finding the optimal fitting by introducing a hearing aid speech processing framework, in which the fitting is optimised in an automated way using an intelligibility objective function based on the HASPI physiological auditory model.
no code implementations • 18 Mar 2020 • Ning Ma, Jiajun Bu, Jieyu Yang, Zhen Zhang, Chengwei Yao, Zhi Yu, Sheng Zhou, Xifeng Yan
The shared sub-structures between training classes and test classes are essential in few-shot graph classification.
1 code implementation • 3 Mar 2020 • Mengyuan Lee, Ning Ma, Guanding Yu, Huaiyu Dai
Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced.
no code implementations • 28 Jan 2019 • Ning Ma, Xin Zhao, Mark Bolin
We demonstrate that using these watermark signals together with the new metric in image search ranker can significantly demote the watermarked images during the online image ranking.
no code implementations • 12 Apr 2018 • Ning Ma, Alexey Volkov, Aleksandr Livshits, Pawel Pietrusinski, Houdong Hu, Mark Bolin
We propose a new framework to rank image attractiveness using a novel pairwise deep network trained with a large set of side-by-side multi-labeled image pairs from a web image index.