Z
Zhenbo Luo
Researcher at Samsung
Publications - 21
Citations - 1869
Zhenbo Luo is an academic researcher from Samsung. The author has contributed to research in topics: Object detection & Feature extraction. The author has an hindex of 11, co-authored 20 publications receiving 1131 citations.
Papers
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Proceedings ArticleDOI
Structured Knowledge Distillation for Semantic Segmentation
TL;DR: Zhang et al. as mentioned in this paper investigated the issue of knowledge distillation for training compact semantic segmentation networks by making use of cumbersome networks and proposed to distill the structured knowledge from cumbersome networks into compact networks.
Posted Content
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection.
TL;DR: A novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images using the Region Proposal Network to generate axis-aligned bounding boxes that enclose the texts with different orientations.
Proceedings ArticleDOI
ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT
Nibal Nayef,Fei Yin,Imen Bizid,Hyun-Soo Choi,Yuan Feng,Dimosthenis Karatzas,Zhenbo Luo,Umapada Pal,Christophe Rigaud,Joseph Chazalon,Wafa Khlif,Muhammad Muzzamil Luqman,Jean-Christophe Burie,Cheng-Lin Liu,Jean-Marc Ogier +14 more
TL;DR: This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge, which aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together.
Proceedings ArticleDOI
Monocular Relative Depth Perception with Web Stereo Data Supervision
TL;DR: A simple yet effective method to automatically generate dense relative depth annotations from web stereo images, and an improved ranking loss is introduced to deal with imbalanced ordinal relations, enforcing the network to focus on a set of hard pairs.
Proceedings ArticleDOI
Arbitrary Shape Scene Text Detection With Adaptive Text Region Representation
TL;DR: Recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found, and text regions of arbitrary shapes are detected and represented with adaptive number of boundary Points.