no code implementations • 23 Feb 2024 • Yongzhi Huang, Jinxin Zhu, Haseeb Hassan, Liyilei Su, Jingyu Li, Binding Huang
In this study, we present a label-efficient learning approach using a pre-trained diffusion model for multi-organ segmentation tasks in CT images.
no code implementations • 8 Jan 2024 • Yusheng Tian, Jingyu Li, Tan Lee
Experimental results on a real case of tongue cancer patient confirm that the synthetic voice achieves comparable articulation quality to unimpaired natural speech, while effectively maintaining the target speaker's individuality.
no code implementations • 24 Sep 2023 • Jingyu Li, Tan Lee
The development of deep neural networks (DNN) has significantly enhanced the performance of speaker verification (SV) systems in recent years.
1 code implementation • CVPR 2023 • Wei Hua, Dingkang Liang, Jingyu Li, Xiaolong Liu, Zhikang Zou, Xiaoqing Ye, Xiang Bai
Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years.
1 code implementation • 9 Mar 2023 • Jingyu Li, Zhe Liu, Jinghua Hou, Dingkang Liang
In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D.
no code implementations • ICCV 2023 • Dingyuan Zhang, Dingkang Liang, Zhikang Zou, Jingyu Li, Xiaoqing Ye, Zhe Liu, Xiao Tan, Xiang Bai
Advanced 3D object detection methods usually rely on large-scale, elaborately labeled datasets to achieve good performance.
no code implementations • 31 Oct 2022 • Jingyu Li, Yusheng Tian, Tan Lee
The weights are imposed on the input features to improve the representation ability for speaker modeling.
no code implementations • 31 Oct 2022 • Jingyu Li, Wei Liu, Zhaoyang Zhang, Jiong Wang, Tan Lee
Experimental results on VoxCeleb show that weight quantization is effective for compressing SV models.
no code implementations • 22 Aug 2022 • Yuqing Wang, Xiangxian Li, Zhuang Qi, Jingyu Li, Xuelong Li, Xiangxu Meng, Lei Meng
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features.
1 code implementation • 7 Jul 2022 • Kaiming Kuang, Li Zhang, Jingyu Li, Hongwei Li, Jiajun Chen, Bo Du, Jiancheng Yang
The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing.
no code implementations • 26 Jun 2022 • Yusheng Tian, Jingyu Li, Tan Lee
Pooling is needed to aggregate frame-level features into utterance-level representations for speaker modeling.
no code implementations • 15 Jun 2022 • Jingyu Li, Yusheng Tian, Tan Lee
There is no reason to expect that these features are optimal for all different tasks, including speaker verification (SV).
no code implementations • 15 Jun 2022 • Jingyu Li, Wei Liu, Tan Lee
This paper proposes a domain transfer network, named EDITnet, to alleviate the language-mismatch problem on speaker embeddings without requiring speaker labels.
no code implementations • 25 May 2022 • Wei Liu, Jingyu Li, Tan Lee
The performance of child speech recognition is generally less satisfactory compared to adult speech due to limited amount of training data.
no code implementations • 4 Oct 2021 • Ying Qin, Wei Liu, Zhiyuan Peng, Si-Ioi Ng, Jingyu Li, Haibo Hu, Tan Lee
Input to these classifiers are speech transcripts produced by automatic speech recognition (ASR) models.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 20 Sep 2021 • Jingyu Li, Si-Ioi Ng, Tan Lee
Given the embeddings from a pair of input utterances, a graph model is designed to incorporate additional information from a group of embeddings representing the so-called auxiliary speakers.
no code implementations • 16 Jun 2021 • Si-Ioi Ng, Cymie Wing-Yee Ng, Jingyu Li, Tan Lee
This paper investigates a neural network based approach to detecting consonant errors in disordered speech using consonant-vowel (CV) diphone segment in comparison to using consonant monophone segment.
no code implementations • ICCV 2019 • Zhaoyang Zhang, Jingyu Li, Wenqi Shao, Zhanglin Peng, Ruimao Zhang, Xiaogang Wang, Ping Luo
ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.
no code implementations • 22 Jul 2019 • Ping Luo, Ruimao Zhang, Jiamin Ren, Zhanglin Peng, Jingyu Li
Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer?
1 code implementation • CVPR 2019 • Wenqi Shao, Tianjian Meng, Jingyu Li, Ruimao Zhang, Yudian Li, Xiaogang Wang, Ping Luo
Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.
no code implementations • 16 Jul 2018 • Ruimao Zhang, Hongbin Sun, Jingyu Li, Yuying Ge, Liang Lin, Ping Luo, Xiaogang Wang
To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).
3 code implementations • ICLR 2019 • Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li
We hope SN will help ease the usage and understand the normalization techniques in deep learning.