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Jingtuo Liu

Researcher at Baidu

Publications -  73
Citations -  2461

Jingtuo Liu is an academic researcher from Baidu. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 18, co-authored 59 publications receiving 1293 citations. Previous affiliations of Jingtuo Liu include University of Hong Kong & York University.

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Proceedings ArticleDOI

The Seventh Visual Object Tracking VOT2019 Challenge Results

Matej Kristan, +179 more
TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Posted Content

Targeting Ultimate Accuracy: Face Recognition via Deep Embedding

TL;DR: A two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low dimensional but very discriminative features for face verification and recognition is proposed, showing a clear path to practical high-performance face recognition systems in real world.
Book ChapterDOI

PyramidBox: A Context-Assisted Single Shot Face Detector

TL;DR: Zhang et al. as discussed by the authors proposed a context-assisted single shot face detector, named PyramidBox, to handle the hard face detection problem, which improves the utilization of contextual information in the following three aspects.
Proceedings ArticleDOI

Towards Accurate Scene Text Recognition With Semantic Reasoning Networks

TL;DR: Zhang et al. as discussed by the authors proposed a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission.
Proceedings ArticleDOI

ACFNet: Attentional Class Feature Network for Semantic Segmentation

TL;DR: ACFNet as mentioned in this paper proposes a coarse-to-fine segmentation network, which can be composed of an ACF module and any off-the-shell segmentation networks (base network).