activity
20172019
most citedDeep Heterogeneous Feature Fusion for Template-Based Face Recognition

12 citations · 12 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV2019

Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Zhenyu Wu, Karthik Suresh, Priya Narayanan +3

Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained…

cs.CV2018

Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection

Hongyu Xu, Xutao Lv, Xiaoyu Wang +3

In this paper, we propose a novel object detection algorithm named "Deep Regionlets" by integrating deep neural networks and a conventional detection schema for accurate generic ob…

cs.CV2018

A Fast and Accurate System for Face Detection, Identification, and Verification

Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng +7

The availability of large annotated datasets and affordable computation power have led to impressive improvements in the performance of CNNs on various object detection and recogni…

cs.CV2018

Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning

Hongyu Xu, Jingjing Zheng, Azadeh Alavi +1

In real-world visual recognition problems, the assumption that the training data (source domain) and test data (target domain) are sampled from the same distribution is often viola…

cs.CV2018

Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition

Rajeev Ranjan, Ankan Bansal, Hongyu Xu +4

In recent years, the performance of face verification and recognition systems based on deep convolutional neural networks (DCNNs) has significantly improved. A typical pipeline for…

cs.CV201712 cited

Deep Heterogeneous Feature Fusion for Template-Based Face Recognition

Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu +3

Although deep learning has yielded impressive performance for face recognition, many studies have shown that different networks learn different feature maps: while some networks ar…