activity
20162026
most citedMulti-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification

79 citations · 147 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2026

AUCH-Net: Action Unit-Based Consistency-Aware Hypergraph Network for Cross-Domain Few-Shot Facial Expression Recognition

Xinhan Qiu, Yan Yan, Rui Zhu +2

Recently, cross-domain few-shot facial expression recognition (CF-FER) has received considerable attention. However, the performance of existing CF-FER methods is still unsatisfact…

cs.CV2022★ 66 cited

Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes

Xi Weng, Yan Yan, Si Chen +2

Over the past few years, deep convolutional neural network-based methods have made great progress in semantic segmentation of street scenes. Some recent methods align feature maps…

cs.CV2022

When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework

Xinyi Zou, Yan Yan, Jing-Hao Xue +2

Human emotions involve basic and compound facial expressions. However, current research on facial expression recognition (FER) mainly focuses on basic expressions, and thus fails t…

cs.CV2020

Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions

Wanxiang Yang, Yan Yan, Si Chen

Person re-identification (ReID) under occlusions is a challenging problem in video surveillance. Most of existing person ReID methods take advantage of local features to deal with…

cs.CV2020

Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation

Yihan Du, Yan Yan, Si Chen +1

In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs…

cs.CV2020

Joint Deep Learning of Facial Expression Synthesis and Recognition

Yan Yan, Ying Huang, Si Chen +2

Recently, deep learning based facial expression recognition (FER) methods have attracted considerable attention and they usually require large-scale labelled training data. Nonethe…