activity
20092022
most citedMulti-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification

79 citations · 639 across the 41 of their papers we have counts for

collaborators

41 papers

cs.CV2022★ 43 cited

Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes

Xi Weng, Yan Yan, Genshun Dong +4

Real-time semantic segmentation, which aims to achieve high segmentation accuracy at real-time inference speed, has received substantial attention over the past few years. However,…

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.CV2021★ 1 cited

Event Data Association via Robust Model Fitting for Event-based Object Tracking

Haosheng Chen, Yue Wu, Yidong Peng

Event-based approaches, which are based on bio-inspired asynchronous event cameras, have achieved promising performance on various computer vision tasks. However, the study of the…

cs.CV2021★ 13 cited

TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency

Lin Cheng, Pengfei Fang, Yanjie Liang +3

The explanation for deep neural networks has drawn extensive attention in the deep learning community over the past few years. In this work, we study the visual saliency, a.k.a. vi…

cs.CV2021★ 11 cited

Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition

Delian Ruan, Yan Yan, Shenqi Lai +3

In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as…