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Wei Wu

Researcher at SenseTime

Publications -  67
Citations -  8664

Wei Wu is an academic researcher from SenseTime. The author has contributed to research in topics: Computer science & Video tracking. The author has an hindex of 24, co-authored 63 publications receiving 4658 citations. Previous affiliations of Wei Wu include Nanyang Technological University & University of Science and Technology of China.

Papers
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Proceedings ArticleDOI

High Performance Visual Tracking with Siamese Region Proposal Network

TL;DR: The Siamese region proposal network (Siamese-RPN) is proposed which is end-to-end trained off-line with large-scale image pairs for visual object tracking and consists of SiAMESe subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch.
Proceedings ArticleDOI

SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks

TL;DR: This work proves the core reason Siamese trackers still have accuracy gap comes from the lack of strict translation invariance, and proposes a new model architecture to perform depth-wise and layer-wise aggregations, which not only improves the accuracy but also reduces the model size.
Book ChapterDOI

Distractor-aware Siamese Networks for Visual Object Tracking

TL;DR: Zhang et al. as discussed by the authors proposed a distractor-aware Siamese network for accurate and long-term tracking, which uses an effective sampling strategy to control the distribution of training data and make the model focus on the semantic distractors.
Posted Content

Distractor-aware Siamese Networks for Visual Object Tracking

TL;DR: This paper focuses on learning distractor-aware Siamese networks for accurate and long-term tracking, and extends the proposed approach for long- term tracking by introducing a simple yet effective local-to-global search region strategy.
Book ChapterDOI

The sixth visual object tracking VOT2018 challenge results

Matej Kristan, +158 more
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.