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Open Access

Key-Part Attention Retrieval for Robotic Object Recognition

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

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.

Keywords

key-part attentionretrievalrobotic object recognition

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Tsinghua Science and Technology
Volume 29 Issue 3,
June 2024
Pages 644-655
DOI: 10.26599/TST.2023.9010022
Cite this article:
Liu J, Cao Z, Tang Y. Key-Part Attention Retrieval for Robotic Object Recognition. Tsinghua Science and Technology, 2024, 29(3): 644-655. https://doi.org/10.26599/TST.2023.9010022

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Received: 06 September 2022
Revised: 09 January 2023
Accepted: 21 March 2023
Published: 04 December 2023
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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