no code implementations • ECCV 2020 • Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng
Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.
no code implementations • CVPR 2022 • Jia-Wei Chen, Chia-Mu Yu, Ching-Chia Kao, Tzai-Wei Pang, Chun-Shien Lu
Despite an increased demand for valuable data, the privacy concerns associated with sensitive datasets present a barrier to data sharing.
1 code implementation • CVPR 2021 • Jia-Wei Chen, Li-Ju Chen, Chia-Mu Yu, Chun-Shien Lu
However, the sensitive information in the datasets discourages data owners from releasing these datasets.
no code implementations • 22 Jul 2020 • Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.
no code implementations • 22 Jul 2020 • Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng
Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.
no code implementations • 20 Jul 2020 • Yuexiang Li, Jia-Wei Chen, Xinpeng Xie, Kai Ma, Yefeng Zheng
A novel pseudo-label (namely self-loop uncertainty), generated by recurrently optimizing the neural network with a self-supervised task, is adopted as the ground-truth for the unlabeled images to augment the training set and boost the segmentation accuracy.
no code implementations • 29 May 2020 • Rongfang Wang, Fan Ding, Licheng Jiao, Jia-Wei Chen, Bo Liu, Wenping Ma, Mi Wang
We verify our light-weighted neural network on four sets of bitemporal SAR images.
no code implementations • 22 May 2020 • Jia-Wei Chen, Rongfang Wang, Fan Ding, Bo Liu, Licheng Jiao, Jie Zhang
Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset.
no code implementations • 2 Mar 2020 • Hiroki Kawai, Jia-Wei Chen, Prakash Ishwar, Janusz Konrad
We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information.
1 code implementation • 12 Sep 2019 • Tingle Li, Jia-Wei Chen, Haowen Hou, Ming Li
Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source separation.
Ranked #23 on Music Source Separation on MUSDB18
no code implementations • 19 Jun 2019 • Rongfang Wang, Jie Zhang, Jia-Wei Chen, Licheng Jiao, Mi Wang
Change detection is a quite challenging task due to the imbalance between unchanged and changed class.
no code implementations • 19 Jun 2019 • Rongfang Wang, Jia-Wei Chen, Yule Wang, Licheng Jiao, Mi Wang
In this letter, we proposed a spatial metric learning method to obtain a difference image more robust to the speckle by learning a metric from a set of constraint pairs.
no code implementations • 5 Jun 2019 • Yu Chen, Jia-Wei Chen, Dong Wei, Yuexiang Li, Yefeng Zheng
Two approaches are widely used in the literature to fuse multiple modalities in the segmentation networks: early-fusion (which stacks multiple modalities as different input channels) and late-fusion (which fuses the segmentation results from different modalities at the very end).
2 code implementations • 31 Jan 2019 • Sheng Zhou, Jiajun Bu, Xin Wang, Jia-Wei Chen, Can Wang
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
no code implementations • CVPR 2018 • Jingwen Chen, Jia-Wei Chen, Hongyang Chao, Ming Yang
In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images.