1 code implementation • ICCV 2019 • Xiao Jin, Baoyun Peng, Yi-Chao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Xiaolin Hu
However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.
2 code implementations • ICCV 2019 • Baoyun Peng, Xiao Jin, Jiaheng Liu, Shunfeng Zhou, Yi-Chao Wu, Yu Liu, Dongsheng Li, Zhaoning Zhang
Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level.
no code implementations • 28 Feb 2019 • Yingcheng Su, Shunfeng Zhou, Yi-Chao Wu, Tian Su, Ding Liang, Jiaheng Liu, Dixin Zheng, Yingxu Wang, Junjie Yan, Xiaolin Hu
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.
no code implementations • 2 Jun 2018 • Yi-Chao Wu, Fei Yin, Xu-Yao Zhang, Li Liu, Cheng-Lin Liu
Scene text recognition has drawn great attentions in the community of computer vision and artificial intelligence due to its challenges and wide applications.
no code implementations • 28 Nov 2017 • Yang Feng, Yi-Chao Wu, Leonard Stefanski
As a first step, we propose a fast screening method based on the favored smoothing bandwidth of the marginal local constant regression.
no code implementations • 6 Sep 2017 • Fei Yin, Yi-Chao Wu, Xu-Yao Zhang, Cheng-Lin Liu
In this paper, we investigate the intrinsic characteristics of text recognition, and inspired by human cognition mechanisms in reading texts, we propose a scene text recognition method with character models on convolutional feature map.