activity
20172022
most citedA Two-Stage Attentive Network for Single Image Super-Resolution

85 citations · 175 across the 18 of their papers we have counts for

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20 papers · 1 filter

cs.CV20221 cited

Explore Contextual Information for 3D Scene Graph Generation

Yuanyuan Liu, Chengjiang Long, Zhaoxuan Zhang +4

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually…

cs.CV20221 cited

Social Interpretable Tree for Pedestrian Trajectory Prediction

Liushuai Shi, Le Wang, Chengjiang Long +4

Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social I…

cs.CV20228 cited

Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction

Tiezheng Ma, Yongwei Nie, Chengjiang Long +2

This paper presents a high-quality human motion prediction method that accurately predicts future human poses given observed ones. Our method is based on the observation that a goo…

cs.CV2022

CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation

Yu Qiao, Jincheng Zhu, Chengjiang Long +4

Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotat…

cs.CV20216 cited

DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer

Wenju Xu, Chengjiang Long, Ruisheng Wang +1

The paper proposes a Dynamic ResBlock Generative Adversarial Network (DRB-GAN) for artistic style transfer. The style code is modeled as the shared parameters for Dynamic ResBlocks…

cs.CV20211 cited

Luminance Attentive Networks for HDR Image and Panorama Reconstruction

Hanning Yu, Wentao Liu, Chengjiang Long +3

It is very challenging to reconstruct a high dynamic range (HDR) from a low dynamic range (LDR) image as an ill-posed problem. This paper proposes a luminance attentive network nam…