54 citations · 56 across the 3 of their papers we have counts for
7 papers
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…
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…
OVC-Net: Object-Oriented Video Captioning with Temporal Graph and Detail Enhancement
Fangyi Zhu, Jenq-Neng Hwang, Zhanyu Ma +2
Traditional video captioning requests a holistic description of the video, yet the detailed descriptions of the specific objects may not be available. Without associating the movin…