no code implementations • ROCLING 2021 • Wei-Ting Lin, Yu-jia Zhang, Chia-Ping Chen, Chung-Li Lu, Bo-Cheng Chan
Based on a trainable speaker verification system, we use domain generalization algorithms to fine-tune the model parameters.
no code implementations • 29 Sep 2020 • Jinting Wu, Yu-jia Zhang, Xiaoguang Zhao
Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent.
no code implementations • 29 Apr 2019 • Yu-jia Zhang, Kuangyan Song, Yiming Sun, Sarah Tan, Madeleine Udell
Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms.
no code implementations • 17 Jul 2018 • Yu-jia Zhang, Michael Kampffmeyer, Xiaodan Liang, Min Tan, Eric P. Xing
Video summarization plays an important role in video understanding by selecting key frames/shots.
3 code implementations • CVPR 2019 • Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yu-jia Zhang, Eric P. Xing
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.
no code implementations • 30 Apr 2018 • Yu-jia Zhang, Michael Kampffmeyer, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing
Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner.
no code implementations • 20 Apr 2018 • Michael Kampffmeyer, Nanqing Dong, Xiaodan Liang, Yu-jia Zhang, Eric P. Xing
We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair based connectivity prediction task.
no code implementations • 2 Jan 2018 • Yu-jia Zhang, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing
Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i. e., objects of interest and their key motions) in online videos has been barely touched.
no code implementations • 30 May 2015 • Yi-bin Huang, Kang Li, Ge Wang, Min Cao, Pin Li, Yu-jia Zhang
For the problem whether Graphic Processing Unit(GPU), the stream processor with high performance of floating-point computing is applicable to neural networks, this paper proposes the parallel recognition algorithm of Convolutional Neural Networks(CNNs). It adopts Compute Unified Device Architecture(CUDA)technology, definite the parallel data structures, and describes the mapping mechanism for computing tasks on CUDA.