1 code implementation • 24 Nov 2023 • Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corporations, and hidden behind paywalls.
1 code implementation • ICCV 2023 • Ruoyi Du, Wenqing Yu, Heqing Wang, Ting-En Lin, Dongliang Chang, Zhanyu Ma
Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of history, it is still limited to recognizing 2 images.
Fine-Grained Image Classification Fine-Grained Visual Recognition
no code implementations • CVPR 2023 • Dongliang Chang, Yujun Tong, Ruoyi Du, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets.
1 code implementation • CVPR 2023 • Ruoyi Du, Dongliang Chang, Kongming Liang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
Our code is available at https://github. com/PRIS-CV/On-the-fly-Category-Discovery.
1 code implementation • 30 Nov 2022 • Jijie Wu, Dongliang Chang, Aneeshan Sain, Xiaoxu Li, Zhanyu Ma, Jie Cao, Jun Guo, Yi-Zhe Song
Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i. e., lower inter-class variations and higher intra-class variations).
1 code implementation • 2 Jun 2022 • Ruoyi Du, Wenqing Yu, Heqing Wang, Dongliang Chang, Ting-En Lin, Yongbin Li, Zhanyu Ma
As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences.
no code implementations • 20 Jan 2022 • Jingye Wang, Ruoyi Du, Dongliang Chang, Kongming Liang, Zhanyu Ma
Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i. i. d.
1 code implementation • 6 Dec 2021 • Dongliang Chang, Kaiyue Pang, Ruoyi Du, Zhanyu Ma, Yi-Zhe Song, Jun Guo
1 lays out our approach in answering this question.
1 code implementation • 6 Dec 2021 • Ruoyi Du, Dongliang Chang, Zhanyu Ma, Yi-Zhe Song, Jun Guo
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for.
2 code implementations • 31 Jan 2021 • Dongliang Chang, Yixiao Zheng, Zhanyu Ma, Ruoyi Du, Kongming Liang
Finally, we can obtain multiple discriminative regions on high-level feature channels and obtain multiple more minute regions within these discriminative regions on middle-level feature channels.
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
1 code implementation • 21 Jan 2021 • Tian Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo
Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category.
Ranked #32 on Fine-Grained Image Classification on FGVC Aircraft
1 code implementation • 21 Dec 2020 • Siqing Zhang, Ruoyi Du, Dongliang Chang, Zhanyu Ma, Jun Guo
Convolution neural networks (CNNs), which employ the cross entropy loss (CE-loss) as the loss function, show poor performance since the model can only learn the most discriminative part and ignore other meaningful regions.
Ranked #37 on Fine-Grained Image Classification on CUB-200-2011
1 code implementation • CVPR 2021 • Dongliang Chang, Kaiyue Pang, Yixiao Zheng, Zhanyu Ma, Yi-Zhe Song, Jun Guo
For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo".
Ranked #16 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 12 Oct 2020 • Zeyu Song, Dongliang Chang, Zhanyu Ma, Xiaoxu Li, Zheng-Hua Tan
The loss function is a key component in deep learning models.
1 code implementation • 20 Apr 2020 • Xiaoxu Li, Dongliang Chang, Zhanyu Ma, Zheng-Hua Tan, Jing-Hao Xue, Jie Cao, Jingyi Yu, Jun Guo
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.
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.
1 code implementation • 9 Mar 2020 • Junhui Yin, Siqing Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo
This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD).
2 code implementations • 8 Mar 2020 • Dongliang Chang, Aneeshan Sain, Zhanyu Ma, Yi-Zhe Song, Jun Guo
The key insight lies with how we exploit the mutually beneficial information between two networks; (a) to separate samples of known and unknown classes, (b) to maximize the domain confusion between source and target domain without the influence of unknown samples.
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.
1 code implementation • 25 Dec 2019 • Ke Zhang, Yurong Guo, Xinsheng Wang, Dongliang Chang, Zhenbing Zhao, Zhanyu Ma, Tony X. Han
However, during the training of the deep convolutional neural network, the value of NLLR is not always positive or negative, which severely affects the convergence of NLLR.
no code implementations • 14 Feb 2019 • Zhanyu Ma, Dongliang Chang, Xiaoxu Li
Experimental results on two fine-grained vehicle datasets, the Stanford Cars-196 dataset and the Comp Cars dataset, demonstrate that the proposed layer could improve classification accuracies of deep neural networks on fine-grained vehicle classification in the situation that a massive of parameters are reduced.