activity
20172022
most citedData Augmentation in Emotion Classification Using Generative Adversarial Networks

78 citations · 88 across the 10 of their papers we have counts for

collaborators

23 papers

cs.CV2022

Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention

Yifan Liu, Youbao Tang, Ning Zhang +2

Temporal action localization (TAL) aims to detect the boundary and identify the class of each action instance in a long untrimmed video. Current approaches treat video frames homog…

cs.CV20221 cited

Efficient Video Segmentation Models with Per-frame Inference

Yifan Liu, Chunhua Shen, Changqian Yu +1

Most existing real-time deep models trained with each frame independently may produce inconsistent results across the temporal axis when tested on a video sequence. A few methods t…

cs.CV2021

A Generative Adversarial Framework for Optimizing Image Matting and Harmonization Simultaneously

Xuqian Ren, Yifan Liu, Chunlei Song

Image matting and image harmonization are two important tasks in image composition. Image matting, aiming to achieve foreground boundary details, and image harmonization, aiming to…

cs.CV20216 cited

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Bowen Zhang, Yifan Liu, Zhi Tian +1

Semantic segmentation requires per-pixel prediction for a given image. Typically, the output resolution of a segmentation network is severely reduced due to the downsampling operat…

cs.CV2021

X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering

Jingjing Jiang, Ziyi Liu, Yifan Liu +2

Encouraging progress has been made towards Visual Question Answering (VQA) in recent years, but it is still challenging to enable VQA models to adaptively generalize to out-of-dist…

cs.CV2021

A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

Jianlong Yuan, Yifan Liu, Chunhua Shen +2

Recently, significant progress has been made on semantic segmentation. However, the success of supervised semantic segmentation typically relies on a large amount of labelled data,…