Key-Part Attention Retrieval for Robotic Object Recognition
文献类型：期刊论文
| 作者 | Liu, Jierui1,2 ; Cao, Zhiqiang1,2; Tang, Yingbo1,2
|
| 刊名 | TSINGHUA SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 29期号:3页码:644-655 |
| ISSN号 | 1007-0214 |
| 关键词 | Training Visualization Image recognition Cameras Object recognition Convolutional neural networks Data mining key-part attention retrieval robotic object recognition |
| DOI | 10.26599/TST.2023.9010022 |
| 通讯作者 | Cao, Zhiqiang(zhiqiang.cao@ia.ac.cn) |
| 英文摘要 | The ability to recognize novel objects with a few visual samples is critical in the robotic applications. Existing methods mainly concern the recognition of inter-category objects, however, the object recognition from different sub-classes within the same category remains challenging due to their similar appearances. In this paper, we propose a key-part attention retrieval solution to distinguish novel objects of different sub-classes according to a few samples without re-training. Especially, an object encoder, including convolutional neural network with attention and key-part aggregation, is designed to generate object attention map and extract the object-level embedding, where object attention map from the middle stage of the backbone is used to guide the key-part aggregation. Besides, to overcome the non-differentiability drawback of key-part attention, the object encoder is trained in a two-step scheme, and a more stable object-level embedding is obtained. On this basis, the potential objects are located from a scene image by mining connected domains of the attention map. By matching the embedding of each potential object and embeddings from support data, the recognition of the potential objects is achieved. The effectiveness of the proposed method is verified by experiments. |
| WOS关键词 | IMAGE ; PATTERNS |
| 资助项目 | National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61973302] ; CIE-Tencent Robotics X Rhino-Bird Focused Research Program[2022-07] ; Beijing Natural Science Foundation[2022MQ05] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | TSINGHUA UNIV PRESS |
| WOS记录号 | WOS:001123318200007 |
| 资助机构 | National Natural Science Foundation of China ; CIE-Tencent Robotics X Rhino-Bird Focused Research Program ; Beijing Natural Science Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54988]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Cao, Zhiqiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jierui,Cao, Zhiqiang,Tang, Yingbo. Key-Part Attention Retrieval for Robotic Object Recognition[J]. TSINGHUA SCIENCE AND TECHNOLOGY,2024,29(3):644-655. |
| APA | Liu, Jierui,Cao, Zhiqiang,&Tang, Yingbo.(2024).Key-Part Attention Retrieval for Robotic Object Recognition.TSINGHUA SCIENCE AND TECHNOLOGY,29(3),644-655. |
| MLA | Liu, Jierui,et al."Key-Part Attention Retrieval for Robotic Object Recognition".TSINGHUA SCIENCE AND TECHNOLOGY 29.3(2024):644-655. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。