158 citations · 343 across the 24 of their papers we have counts for
9 papers · 1 filter
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,…
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Jiyang Xie, Zhanyu Ma, and Jianjun Lei +4
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate o…
SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification
Junhui Yin, Jiayan Qiu, Siqing Zhang +2
Domain adaptive person re-identification (re-ID) is a challenging task due to the large discrepancy between the source domain and the target domain. To reduce the domain discrepanc…
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Xiaoxu Li, Liyun Yu, Xiaochen Yang +4
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the samp…
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…