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20102023
most citedSession-based Recommendation with Graph Neural Networks

1.5k citations · 3.2k across the 75 of their papers we have counts for

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Showing 2019 · cs.CVShow all

9 papers · 2 filters

cs.CV2019

Alignment Free and Distortion Robust Iris Recognition

Min Ren, Caiyong Wang, Yunlong Wang +2

Iris recognition is a reliable personal identification method but there is still much room to improve its accuracy especially in less-constrained situations. For example, free move…

cs.CV2019★ 4 cited

Dynamic Graph Representation for Partially Occluded Biometrics

Min Ren, Yunlong Wang, Zhenan Sun +1

The generalization ability of Convolutional neural networks (CNNs) for biometrics drops greatly due to the adverse effects of various occlusions. To this end, we propose a novel un…

cs.CV2019★ 6 cited

A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging

Yunfan Liu, Qi Li, Zhenan Sun +1

Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years. Although great progres…

cs.CV2019★ 49 cited

Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection

Junran Peng, Ming Sun, Zhaoxiang Zhang +2

Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for…

cs.CV2019

POD: Practical Object Detection with Scale-Sensitive Network

Junran Peng, Ming Sun, Zhaoxiang Zhang +2

Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance. In addition, the…

cs.CV2019★ 1 cited

Progressive Cluster Purification for Transductive Few-shot Learning

Chenyang Si, Wentao Chen, Wei Wang +2

Few-shot learning aims to learn to generalize a classifier to novel classes with limited labeled data. Transductive inference that utilizes unlabeled test set to deal with low-data…