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Visual affordance detection using an efficient attention convolutional neural network | |
Gu, Qipeng1,2![]() ![]() | |
发表期刊 | NEUROCOMPUTING
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ISSN | 0925-2312 |
2021-06-14 | |
卷号 | 440期号:2021页码:36-44 |
摘要 | Visual affordance detection is an important issue in the field of robotics and computer vision. This paper proposes a novel and practical convolutional neural network architecture that adopts an encoder-decoder architecture for pixel-wise affordance detection. The encoder network comprises two modules: a dilated residual network that is the backbone for feature extraction, and an attention mechanism that is used for modeling long-range, multi-level dependency relations. The decoder network consists of a novel up sampling layer that maps the low-resolution encoder feature to a high-resolution pixel-wise prediction map. Specifically, integrating an attention mechanism into our network reduces the loss of salient details and improves the feature representation performance of the model. The results of experiments conducted on the University of Maryland dataset (UMD) verify that the proposed network with the attention mechanism and up-sampling layer improved performance compared with classical methods. The proposed method lays the foundation for subsequent research on multi-task learning by physical robots. |
关键词 | Affordance detection Attention mechanism Up-sampling layer |
DOI | 10.1016/j.neucom.2021.01.018 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | NSFC[91848109] ; Beijing Natural Science Foundation[4182068] ; Science and Technology on Space Intelligent Control Laboratory[HTKJ2019KL502013] ; State Key Laboratory of Rail Traffic Control and Safety[RS2018K009] ; Beijing Jiaotong University ; Major scientific and technological innovation projects in Shandong Province[2019JZZY010430] |
项目资助者 | NSFC ; Beijing Natural Science Foundation ; Science and Technology on Space Intelligent Control Laboratory ; State Key Laboratory of Rail Traffic Control and Safety ; Beijing Jiaotong University ; Major scientific and technological innovation projects in Shandong Province |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000642408200005 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 机器人感知与决策 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44640 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Su, Jianhua |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Beijing Jiaotong Univ, State key Lab Rail Traff Control & Safety, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Gu, Qipeng,Su, Jianhua,Yuan, Lei. Visual affordance detection using an efficient attention convolutional neural network[J]. NEUROCOMPUTING,2021,440(2021):36-44. |
APA | Gu, Qipeng,Su, Jianhua,&Yuan, Lei.(2021).Visual affordance detection using an efficient attention convolutional neural network.NEUROCOMPUTING,440(2021),36-44. |
MLA | Gu, Qipeng,et al."Visual affordance detection using an efficient attention convolutional neural network".NEUROCOMPUTING 440.2021(2021):36-44. |
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