سال انتشار:

2020

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عنوان انگلیسی مقاله:

Multi-attention deep reinforcement learning and re-ranking for vehicle re-identification

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ترجمه فارسی عنوان مقاله:

یادگیری تقویتی عمیق چند منظوره و رتبه بندی مجدد برای شناسایی مجدد خودرو

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منبع:

Sciencedirect - Elsevier - Neurocomputing, 414 (2020) 27-35. doi:10.1016/j.neucom.2020.07.020

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نویسنده:

Yu Liu a, Jianbing Shen a,⇑, Haibo He b

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چکیده انگلیسی:

For solving the vehicle Re-identification (Re-ID) task, we need to focus our attention on the details with arbitrary size in the image, and it’s tough to locate these details accurately. In this paper, we propose a Multi-Attention Deep Reinforcement Learning (MADRL) model to focus on multi-attentional subregions that spreading randomly in the image, and extract the discriminative features for the Re-ID task. First, we obtain multiple attentions from the representative features, then group the feature channels into different parts, then train a deep reinforcement learning model to learn more accurate positions of these fine-grained details with different losses. Unlike existing models with complex strategies to keep the patch-matching constrains, our MADRL model can automatically locate the matching patches (multiattentional subregions) in different vehicle images with the same identification (ID). Furthermore, based on the fine-grained attention and global features we re-calculate the distance between the inter- and intra- classes, and we get better re-ranking results. Compared with state-of-the-art methods on three large-scale vehicle Re-ID datasets, our algorithm greatly improves the performance of vehicle Re-ID.
Keywords: Re-identification | Deep reinforcement learning | Multi-attention | Re-ranking

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قیمت: رایگان

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توضیحات اضافی:

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