1 code implementation • ICCV 2023 • Qihua Dong, Hao Du, Ying Song, Yan Xu, Jing Liao
Our approach balances image similarity and volume preservation in different regions, i. e., normal and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one.
no code implementations • 4 Feb 2023 • Hao Du, Qihua Dong, Yan Xu, Jing Liao
Furthermore, we propose contrastive similarity to encourage organ pixels to gather around in the contrastive embedding space, which helps better distinguish low-contrast tissues.
no code implementations • CVPR 2023 • Yuchen Ren, Zhendong Mao, Shancheng Fang, Yan Lu, Tong He, Hao Du, Yongdong Zhang, Wanli Ouyang
In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process.
no code implementations • 4 Aug 2021 • Fangzhou Han, Can Wang, Hao Du, Jing Liao
To address this, we present a novel deep learning framework for portrait lighting enhancement based on 3D facial guidance.
no code implementations • 14 May 2021 • Hao Du, Melissa Min-Szu Yao, Liangyu Chen, Wing P. Chan, Mengling Feng
In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms.
2 code implementations • NeurIPS 2020 • Houwen Peng, Hao Du, Hongyuan Yu, Qi Li, Jing Liao, Jianlong Fu
The experiments on ImageNet verify such path distillation method can improve the convergence ratio and performance of the hypernetwork, as well as boosting the training of subnetworks.
3 code implementations • 18 Jun 2020 • Hongyuan Yu, Houwen Peng, Yan Huang, Jianlong Fu, Hao Du, Liang Wang, Haibin Ling
First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized.
Ranked #17 on Neural Architecture Search on NAS-Bench-201, CIFAR-10
1 code implementation • 6 Feb 2020 • Hao Du, Jing Guo, Ziming Li, Elaine Wong
We consider the additive decomposition problem in primitive towers and present an algorithm to decompose a function in an S-primitive tower as a sum of a derivative in the tower and a remainder which is minimal in some sense.
Symbolic Computation
no code implementations • 16 Dec 2019 • Hao Du, Jiashi Feng, Mengling Feng
In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination.
no code implementations • Neurocomputing 2019 • Hao Du, Tian Jin, Yuan He, Yongping Song, Yongpeng Dai
In this work, we propose a neural network architecture, namely segmented convolutional gated recurrent neural network (SCGRNN), to recognize human activities based on micro-Doppler spectrograms measured by the ultra-wideband radar.
no code implementations • 14 Jan 2019 • Ziyuan Pan, Hao Du, Kee Yuan Ngiam, Fei Wang, Ping Shum, Mengling Feng
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
1 code implementation • 7 Feb 2018 • Shaoshi Chen, Hao Du, Ziming Li
This paper extends the classical Ostrogradsky-Hermite reduction for rational functions to more general functions in primitive extensions of certain types.
Symbolic Computation 33F10, 68W30, 12H05