415 citations · 795 across the 30 of their papers we have counts for
10 papers · 1 filter
Knowledge Transfer Based Fine-grained Visual Classification
Siqing Zhang, Ruoyi Du, Dongliang Chang +2
Fine-grained visual classification (FGVC) aims to distinguish the sub-classes of the same category and its essential solution is to mine the subtle and discriminative regions. Conv…
Your "Flamingo" is My "Bird": Fine-Grained, or Not
Dongliang Chang, Kaiyue Pang, Yixiao Zheng +3
Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former,…
CC-Loss: Channel Correlation Loss For Image Classification
Zeyu Song, Dongliang Chang, Zhanyu Ma +2
The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective applicatio…
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
Xiaoxu Li, Dongliang Chang, Zhanyu Ma +5
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. To mitigate overfitting…
Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
Dongliang Chang, Aneeshan Sain, Zhanyu Ma +2
Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain. Among its many variants, open set domain ada…
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
Junhui Yin, Siqing Zhang, Dongliang Chang +2
Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of wea…
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
Jiyang Xie, Dongliang Chang, Zhanyu Ma +2
Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to s…
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia +4
Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works ma…
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification
Yifeng Ding, Shaoguo Wen, Jiyang Xie +4
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…
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Dongliang Chang, Yifeng Ding, Jiyang Xie +6
Key for solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex netw…