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Träfflista för sökning "WFRF:(Li Bin) "

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Numrering	Referens	Omslagsbild	Hitta
1.	Klionsky, Daniel J, et al. (författare) Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition). 2016 Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 12:1, s. 1-222 Tidskriftsartikel (refereegranskat)
2.	Ahmad, T, et al. (författare) Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries 2016 Ingår i: British journal of anaesthesia. - : Elsevier BV. - 1471-6771 .- 0007-0912. ; 117:5, s. 601- Tidskriftsartikel (refereegranskat)
3.	Ahmad, T, et al. (författare) In-hospital clinical outcomes after upper gastrointestinal surgery: Data from an international observational study 2017 Ingår i: European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. - : Elsevier BV. - 1532-2157 .- 0748-7983. ; 43:12, s. 2324-2332 Tidskriftsartikel (refereegranskat)
4.	Ahmad, T, et al. (författare) Use of failure-to-rescue to identify international variation in postoperative care in low-, middle- and high-income countries: a 7-day cohort study of elective surgery 2017 Ingår i: British journal of anaesthesia. - : Elsevier BV. - 1471-6771 .- 0007-0912. ; 119:2, s. 258-266 Tidskriftsartikel (refereegranskat)
5.	Kristanl, Matej, et al. (författare) The Seventh Visual Object Tracking VOT2019 Challenge Results 2019 Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241 Konferensbidrag (refereegranskat)abstract 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. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
6.	2019 Tidskriftsartikel (refereegranskat)
7.	Wong, Gane Ka-Shu, et al. (författare) A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms. 2004 Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 432:7018, s. 717-22 Tidskriftsartikel (refereegranskat)
8.	Wright, NJ, et al. (författare) Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study 2021 Ingår i: Lancet (London, England). - 1474-547X. ; 398:10297, s. 325-339 Tidskriftsartikel (refereegranskat)
9.	Kristan, Matej, et al. (författare) The first visual object tracking segmentation VOTS2023 challenge results 2023 Ingår i: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810 Konferensbidrag (refereegranskat)abstract The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
10.	Kristan, Matej, et al. (författare) The Visual Object Tracking VOT2016 Challenge Results 2016 Ingår i: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 777-823 Konferensbidrag (refereegranskat)abstract The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.

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