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- Karamacoska, Diana, et al.
(författare)
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How are early‐career dementia researchers considered and supported on a national level by dementia plans and organizations? An overview of global policy approaches
- 2024
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Ingår i: Alzheimer's & Dementia. - 1552-5260 .- 1552-5279.
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTIONDespite representing an essential workforce, it is unclear how global policy efforts target early-career dementia researchers (ECDRs). Thus, this study aimed to provide an overview of policies through which ECDRs are considered and supported by dementia plans and organizations.METHODSG20 member states were evaluated for their national dementia plan alongside policies of leading dementia organizations. Data targeting support for ECDRs were extracted and subject to content analysis using inductive coding. Findings were categorized and narratively synthesized.RESULTSOnly China, Denmark, England, Greece, Northern Ireland, Scotland, Spain, and the United States mentioned ECDRs in their national plan. Additionally, 17 countries formalized ECDR support via dementia organizations. Support efforts included research funding, dissemination and networking, career development, and research advice.DISCUSSIONFew nations formally recognized ECDRs in dementia plans or through dementia organizations. To facilitate equal prospects for ECDRs, top-down approaches are urged to enhance and align their efforts.
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| 2. |
- Kristanl, Matej, et al.
(författare)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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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).
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| 3. |
- Ma, Runchuan, et al.
(författare)
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Novel View Synthesis and Dataset Augmentation for Hyperspectral Data Using NeRF
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 12, s. 45331-45341
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Tidskriftsartikel (refereegranskat)abstract
- Hyperspectral data for the 3D domain is relatively difficult to acquire. Existing hyperspectral datasets are unsuitable for 3D research, suffer from issues of severe data scarcity, and a lack of multi-perspective images of the same object, etc. To address these challenges, data augmentation with limited data is essential. In this study, we applied neural rendering method (such as Neural Radiance Field) to hyperspectral images for dataset augmentation. We conducted experiments on novel view synthesis for hyperspectral images from 360-degree multi-perspectives, demonstrating that our method can generate high-quality hyperspectral images from various perspectives. Through experiments involving key points extraction and 3D reconstruction, we validated the efficacy of generating a substantial volume of high-quality hyperspectral images from a restricted set of varying perspectives. These results contribute to addressing the challenges associated with data augmentation. We also conducted experiments of neural radiance fields in the hyperspectral data domain under different network parameters and training conditions to find the appropriate settings.
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