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Record W2911372736 · doi:10.1007/978-3-030-11021-5_28

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

2019· book-chapter· en· W2911372736 on OpenAlexaff
Longyin Wen, Pengfei Zhu, Dawei Du, Xiao Bian, Haibin Ling, Qinghua Hu, Chenfeng Liu, Hao Cheng, Xiaoyu Liu, Wenya Ma, Qinqin Nie, Haotian Wu, Lianjie Wang, Asanka G. Perera, Baochang Zhang, Byeongho Heo, Chunlei Liu, Dongdong Li, Emmanouil Michail, Hanlin Chen, Hao Liu, Haojie Li, Ioannis Kompatsiaris, Jian Cheng, Jiaqing Fan, Jie Zhang, Jin Young Choi, Jing Li, Jinyu Yang, Jongwon Choi, Juanping Zhao, Jungong Han, Kaihua Zhang, Kaiwen Duan, Ke Song, Konstantinos Avgerinakis, Kyuewang Lee, Lu Ding, Martin Lauer, Panagiotis Giannakeris, Peizhen Zhang, Qiang Wang, Qianqian Xu, Qingming Huang, Qingshan Liu, Robert Laganière, Ruixin Zhang, Sangdoo Yun, Shengyin Zhu, Sihang Wu, Stefanos Vrochidis, Wei Tian, Wei Zhang, Weidong Chen, Weiming Hu, Wenhao Wang, Wenhua Zhang, Wenrui Ding, Xiaohao He, Xiaotong Li, Xin Zhang, Xinbin Luo, Xixi Hu, Yang Meng, Yangliu Kuai, Yanyun Zhao, Yaxuan Li, Yifan Yang, Yifan Zhang, Yong Wang, Yuankai Qi, Zhipeng Deng, Zhiqun He

Bibliographic record

VenueLecture notes in computer science · 2019
Typebook-chapter
Languageen
FieldComputer Science
TopicVideo Surveillance and Tracking Methods
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceArtificial intelligenceComputer visionClutterBitTorrent trackerDroneVideo trackingTracking (education)Benchmark (surveying)Eye trackingObject (grammar)Bounding overwatchRadarCartography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0070.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.291
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2019
Admission routes1
Has abstractno

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