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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing; 28th International C Igor V. Tetko,Věra Kůrková,Fabian Thei

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https://doi.org/10.1007/978-3-7091-4435-0h ScratchDet for fast evaluation. Moreover, as a pre-trained model on ImageNet, DRFNet is also tested with SSD. All the experiments show that DRFNet is an effective and efficient backbone network for object detection.
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Phase Transitions in Thin Films,square estimation. We integrate the adaptive feature channel weighting scheme into two state-of-the-art handcrafted DCF based trackers, and evaluate them on two benchmarks: OTB2013 and VOT2016, respectively. The experimental results demonstrate its accuracy and efficiency when compared with some state-of-the-art handcrafted DCF based trackers.
发表于 2025-3-29 00:50:22 | 显示全部楼层
Ferroelectric Domains: Some Recent Advances,s show that our network achieves superior performance with the fewest parameters and the fastest speed compared with baseline methods on the IDRiD dataset. Specially, with 1/20 parameters and 1/3 inference time, our method is over 10% higher than DeepLab v3+ in terms of F1-score on the IDRiD dataset. The source code of LWENet is available at ..
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Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluationspatial entropy analysis on eye movement data, a quantitative measure is obtained to show significant correlation with the decision-making efficiency of evaluation processing, which is characterized by evaluation time and decision error. As a result, the new measure may be used as a proxy definition for this decision-making efficiency.
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A New Learning-Based One Shot Detection Framework for Natural Imageser these steps, we can obtain a temporary result. Based on this result and some proposals related to it, we refine the proposals through the intersection. Then we conduct second-round detection with new proposals and improve the accuracy. Experiments on different datasets demonstrate that our method is effective and has a certain transferability.
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An Adaptive Feature Channel Weighting Scheme for Correlation Trackingsquare estimation. We integrate the adaptive feature channel weighting scheme into two state-of-the-art handcrafted DCF based trackers, and evaluate them on two benchmarks: OTB2013 and VOT2016, respectively. The experimental results demonstrate its accuracy and efficiency when compared with some state-of-the-art handcrafted DCF based trackers.
发表于 2025-3-30 07:15:52 | 显示全部楼层
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