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[行人重識別]--ICCV2019-Mixed High-Order Attention Network for Person Re-Identification
時間 2021-01-07
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摘要: 注意力在行人重識別(ReID)中變得更有吸引力,因爲它能夠將可用資源分配給輸入信號中信息最豐富的部分。然而,目前最先進的工作主要集中在粗糙的或一階的注意力設計上,如空間和渠道的注意力,很少探索高階的注意力機制。我們朝着解決這個問題邁出了一步。在本文中,我們首先提出了高階注意(High-Order Attention, HOA)模塊,對注意機制中複雜的高階統計信息進行建模和利用,從而捕捉行人
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相關文章
1.
ICCV2019-行人重識別-Mixed High-Order Attention Network for Person Re-Identification
2.
CVPR2019-行人重識別-Interaction-and-Aggregation Network for Person Re-identification
3.
Collaborative Attention Network for Person Re-identification
4.
Harmonious Attention Network for Person Re-Identification
5.
[行人重識別]--AAAI2020-Relation Network for Person Re-identification
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行人再識別:Mask-guided Contrastive Attention Model for Person Re-Identification
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Paper Reading: Attention-Aware Compositional Network for Person Re-identification
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Three-Dimension Transmissible Attention Network for Person Re-Identification
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Attention-Aware Compositional Network for Person Re-identification
10.
行人再識別:Beyond triplet loss: a deep quadruplet network for person re-identification
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