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Zhenyu He

Researcher at Harbin Institute of Technology

Publications -  157
Citations -  8276

Zhenyu He is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Video tracking & Zircon. The author has an hindex of 41, co-authored 145 publications receiving 5900 citations. Previous affiliations of Zhenyu He include Shenzhen University & South China University of Technology.

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Book ChapterDOI

The Visual Object Tracking VOT2016 Challenge Results

Matej Kristan, +140 more
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Journal ArticleDOI

The crust of Cathaysia: Age, assembly and reworking of two terranes

TL;DR: Zircons from the Oujiang River in eastern and western parts of the Cathaysia block in SE China have been used to analyse the crustal evolution of the Yanshanian magmatism, consistent with mixing between crustal and juvenile magmas as mentioned in this paper.
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.
Journal ArticleDOI

The assembly of Rodinia: The correlation of early Neoproterozoic (ca. 900 Ma) high-grade metamorphism and continental arc formation in the southern Beishan Orogen, southern Central Asian Orogenic Belt (CAOB)

TL;DR: In this article, the authors provided new petrological, geochemical and geochronological data for garnet-bearing schists from the South Beishan Orogenic Belt (SBOB) in order to constrain its Neoproterozoic metamorphic history.
Proceedings ArticleDOI

Target-Aware Deep Tracking

TL;DR: This paper develops a regression loss and a ranking loss to guide the generation of target-active and scale-sensitive features and proposes a novel scheme to learn target-aware features, which can better recognize the targets undergoing significant appearance variations than pre-trained deep features.