activity
20172022
most citedBSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification

217 citations · 416 across the 16 of their papers we have counts for

collaborators

28 papers

cs.CV202266 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…

stat.ML2021

Dimension Reduction for Data with Heterogeneous Missingness

Yurong Ling, Zijing Liu, Jing-Hao Xue

Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard di…

cs.CV2021

SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes

Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3

Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Most of advanced solutions exploit a metri…

cs.CV202122 cited

Towards Open-World Text-Guided Face Image Generation and Manipulation

Weihao Xia, Yujiu Yang, Jing-Hao Xue +1

The existing text-guided image synthesis methods can only produce limited quality results with at most \mbox{} resolution and the textual instructions are constrained…

cs.CV202031 cited

TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

Weihao Xia, Yujiu Yang, Jing-Hao Xue +1

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: S…