6 papers
Detector With Focus: Normalizing Gradient In Image Pyramid
Yonghyun Kim, Bong-Nam Kang, Daijin Kim
An image pyramid can extend many object detection algorithms to solve detection on multiple scales. However, interpolation during the resampling process of an image pyramid causes…
Attentional Feature-Pair Relation Networks for Accurate Face Recognition
Bong-Nam Kang, Yonghyun Kim, Bongjin Jun +1
Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and il…
Pairwise Relational Networks using Local Appearance Features for Face Recognition
Bong-Nam Kang, Yonghyun Kim, Daijin Kim
We propose a new face recognition method, called a pairwise relational network (PRN), which takes local appearance features around landmark points on the feature map, and captures…
BAN: Focusing on Boundary Context for Object Detection
Yonghyun Kim, Taewook Kim, Bong-Nam Kang +2
Visual context is one of the important clue for object detection and the context information for boundaries of an object is especially valuable. We propose a boundary aware network…
Pairwise Relational Networks for Face Recognition
Bong-Nam Kang, Yonghyun Kim, Daijin Kim
Existing face recognition using deep neural networks is difficult to know what kind of features are used to discriminate the identities of face images clearly. To investigate the e…
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
Yonghyun Kim, Bong-Nam Kang, Daijin Kim
Most of the recent successful methods in accurate object detection build on the convolutional neural networks (CNN). However, due to the lack of scale normalization in CNN-based de…