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Jing Li

Researcher at Alibaba Group

Publications -  78
Citations -  1920

Jing Li is an academic researcher from Alibaba Group. The author has contributed to research in topics: Computer science & Image stitching. The author has an hindex of 13, co-authored 67 publications receiving 1197 citations. Previous affiliations of Jing Li include Tianjin University & University of Nantes.

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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.
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

Parallax-Tolerant Image Stitching Based on Robust Elastic Warping

TL;DR: A parallax-tolerant image stitching method based on robust elastic warping, which could achieve accurate alignment and efficient processing simultaneously and is highly compatible with different transformation types.
Proceedings ArticleDOI

DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation

TL;DR: Experiments show that DAML achieves significantly better rating prediction accuracy compared to the state-of-the-art methods, and the attention mechanism can highlight the relevant information in reviews to increase the interpretability of rating prediction.
Book ChapterDOI

VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results

Longyin Wen, +72 more
TL;DR: The evaluation protocol of the VisDrone-SOT2018 challenge is presented and the results of a comparison of 22 trackers on the benchmark dataset are presented, which are publicly available on the challenge website.