Knowledge Commons of Institute of Automation,CAS
Hierarchical Attention Network for Open-Set Fine-Grained Recognition | |
Jiayin, Sun1,2,3![]() ![]() | |
发表期刊 | IEEE Transactions on Circuits and Systems for Video Technology
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2023-10 | |
页码 | 1-14 |
摘要 | Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision community. However, we empirically found that a typical vision transformer with the spatial self-attention mechanism could not learn accurate attention maps for distinguishing different categories of fine-grained images. To address this problem, motivated by the temporal attention mechanism in brains, we propose a hierarchical attention network for learning fine-grained feature representations, called HAN, where the features learnt by implementing a sequence of spatial self-attention operations corresponding to multiple moments are aggregated progressively. The proposed HAN consists of four modules: a self-attention backbone module for learning a sequence of features with selfattention operations, a spatial feature self-organizing module for facilitating the model training, a hierarchical aggregation module for aggregating the re-organized features via a Long Short-Term Memory network, and a context-aware module that is implemented as the forget block of the hierarchical aggregation module for preserving/forgetting the long-term memory by utilizing contextual information. Then, we propose a HAN-based method for open-set fine-grained recognition by integrating the proposed HAN network with a linear classifier, called HAN-OSFGR. Extensive experimental results on 3 fine-grained datasets and 2 coarse-grained datasets demonstrate that the proposed HAN-OSFGR outperforms 9 state-of-the-art open-set recognition methods significantly in most cases. |
收录类别 | SCI |
语种 | 英语 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 多尺度信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56588 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
通讯作者 | Qiulei, Dong |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Coll Life Sci, Beijing 100049, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Jiayin, Sun,Hong, Wang,Qiulei, Dong. Hierarchical Attention Network for Open-Set Fine-Grained Recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology,2023:1-14. |
APA | Jiayin, Sun,Hong, Wang,&Qiulei, Dong.(2023).Hierarchical Attention Network for Open-Set Fine-Grained Recognition.IEEE Transactions on Circuits and Systems for Video Technology,1-14. |
MLA | Jiayin, Sun,et al."Hierarchical Attention Network for Open-Set Fine-Grained Recognition".IEEE Transactions on Circuits and Systems for Video Technology (2023):1-14. |
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Hierarchical_Attenti(2596KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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