1 code implementation • 15 Jul 2022 • Jiyang Xie, Xiu Su, Shan You, Zhanyu Ma, Fei Wang, Chen Qian
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales.
1 code implementation • 25 Mar 2022 • Xiu Su, Shan You, Jiyang Xie, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
1 code implementation • 25 Jun 2021 • Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang, Chang Xu
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks.
no code implementations • 21 Jun 2021 • Chenyu Guo, Jiyang Xie, Kongming Liang, Xian Sun, Zhanyu Ma
Then, attention mechanisms are used after feature fusion to extract spatial and channel information while linking the high-level semantic information and the low-level texture features, which can better locate the discriminative regions for the FGVC.
1 code implementation • 16 Jun 2021 • Wenqing Zheng, Jiyang Xie, Weidong Liu, Zhanyu Ma
For image classification tasks, we propose a structured DropConnect (SDC) framework to model the output of a deep neural network by a Dirichlet distribution.
no code implementations • 1 Apr 2021 • Junhui Yin, Zhanyu Ma, Jiyang Xie, Shibo Nie, Kongming Liang, Jun Guo
Meanwhile, to further mining the relationships between global features from person images, we propose an Affinities Modeling (AM) module to obtain the optimal intra- and inter-modality image matching.
Cross-Modality Person Re-identification Person Re-Identification
no code implementations • 1 Apr 2021 • Junhui Yin, Jiayan Qiu, Siqing Zhang, Jiyang Xie, Zhanyu Ma, Jun Guo
Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models.
no code implementations • 24 Jan 2021 • Shuai Xu, Dongliang Chang, Jiyang Xie, Zhanyu Ma
The proposed method outperforms the SOTA attention modules in the FGVC task.
Ranked #21 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 17 Nov 2020 • Jiyang Xie, Zhanyu Ma, Jing-Hao Xue, Guoqiang Zhang, Jun Guo
In the DS-UI, we combine the classifier of a DNN, i. e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer.
1 code implementation • 11 Oct 2020 • Jiyang Xie, Zhanyu Ma, and Jianjun Lei, Guoqiang Zhang, Jing-Hao Xue, Zheng-Hua Tan, Jun Guo
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs).
1 code implementation • 10 Mar 2020 • Jiyang Xie, Dongliang Chang, Zhanyu Ma, Guo-Qiang Zhang, Jun Guo
In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way.
5 code implementations • ECCV 2020 • Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia, Jiyang Xie, Zhanyu Ma, Yi-Zhe Song, Jun Guo
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
Ranked #17 on Fine-Grained Image Classification on Stanford Cars
3 code implementations • 11 Feb 2020 • Dongliang Chang, Yifeng Ding, Jiyang Xie, Ayan Kumar Bhunia, Xiaoxu Li, Zhanyu Ma, Ming Wu, Jun Guo, Yi-Zhe Song
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #29 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 9 Feb 2020 • Yifeng Ding, Shaoguo Wen, Jiyang Xie, Dongliang Chang, Zhanyu Ma, Zhongwei Si, Haibin Ling
Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.
no code implementations • 2 Aug 2018 • Jiyang Xie, Zeyu Song, Yupeng Li, Zhanyu Ma
Finally, we summarize the main challenges and future development directions of mobile big data analysis.
no code implementations • 2 Aug 2018 • Jiyang Xie, Zhanyu Ma, Jun Guo
Using artificial neural network for the prediction of heat demand has attracted more and more attention.
no code implementations • 28 Jul 2018 • Jiyang Xie, Jiaxin Guo, Zhanyu Ma, Jing-Hao Xue, Qie Sun, Hailong Li, Jun Guo
ENN and ARIMA are used to predict seasonal and trend components, respectively.
no code implementations • 8 Jul 2018 • Jiyang Xie, Zhanyu Ma, Guo-Qiang Zhang, Jing-Hao Xue, Jen-Tzung Chien, Zhiqing Lin, Jun Guo
In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution.