Autonomous novel class discovery for vision-based recognition in non-interactive environments
Visual recognition with deep learning has recently been shown to be effective in robotic vision. However, these algorithms tend to be build under fixed and structured environment, which is rarely the case in real life. When facing unknown objects, avoidance or human interactions are required, which...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2024-01-01
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| Series: | Cognitive Robotics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667241324000156 |
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| _version_ | 1846122016051036160 |
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| author | Xuelin Zhang Feng Liu Xuelian Cheng Siyuan Yan Zhibin Liao Zongyuan Ge |
| author_facet | Xuelin Zhang Feng Liu Xuelian Cheng Siyuan Yan Zhibin Liao Zongyuan Ge |
| author_sort | Xuelin Zhang |
| collection | DOAJ |
| description | Visual recognition with deep learning has recently been shown to be effective in robotic vision. However, these algorithms tend to be build under fixed and structured environment, which is rarely the case in real life. When facing unknown objects, avoidance or human interactions are required, which may miss critical objects or be prohibitively costly to obtain on robots in the real world. We consider a practical problem setting that aims to allow robots to automatically discover novel classes with only labelled known class samples in hand, defined as open-set clustering (OSC). To address the OSC problem, we propose a framework combining three approaches: 1) using selfsupervised vision transformers to mitigate the discard of information needed for clustering unknown classes; 2) adaptive weighting for image patches to prioritize patches with richer textures; and 3) incorporating a temperature scaling strategy to generate more separable feature embeddings for clustering. We demonstrate the efficacy of our approach in six fine-grained image datasets. |
| format | Article |
| id | doaj-art-30de12f518704b5e8becfb2a6b3b6813 |
| institution | Kabale University |
| issn | 2667-2413 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Cognitive Robotics |
| spelling | doaj-art-30de12f518704b5e8becfb2a6b3b68132024-12-15T06:17:54ZengKeAi Communications Co. Ltd.Cognitive Robotics2667-24132024-01-014191203Autonomous novel class discovery for vision-based recognition in non-interactive environmentsXuelin Zhang0Feng Liu1Xuelian Cheng2Siyuan Yan3Zhibin Liao4Zongyuan Ge5Faculty of Information Technology, Monash University, 20 Exhibition Walk, Melbourne 3168, VIC, Australia; Corresponding author.The School of Computing and Information Systems, The University of Melbourne, Parkville, Melbourne 3052, VIC, AustraliaFaculty of Information Technology, Monash University, 20 Exhibition Walk, Melbourne 3168, VIC, AustraliaFaculty of Information Technology, Monash University, 20 Exhibition Walk, Melbourne 3168, VIC, AustraliaFaculty of Sciences, Engineering and Technology, The University of Adelaide, North Terrace, Adelaide, 5001, SA, AustraliaFaculty of Information Technology, Monash University, 20 Exhibition Walk, Melbourne 3168, VIC, AustraliaVisual recognition with deep learning has recently been shown to be effective in robotic vision. However, these algorithms tend to be build under fixed and structured environment, which is rarely the case in real life. When facing unknown objects, avoidance or human interactions are required, which may miss critical objects or be prohibitively costly to obtain on robots in the real world. We consider a practical problem setting that aims to allow robots to automatically discover novel classes with only labelled known class samples in hand, defined as open-set clustering (OSC). To address the OSC problem, we propose a framework combining three approaches: 1) using selfsupervised vision transformers to mitigate the discard of information needed for clustering unknown classes; 2) adaptive weighting for image patches to prioritize patches with richer textures; and 3) incorporating a temperature scaling strategy to generate more separable feature embeddings for clustering. We demonstrate the efficacy of our approach in six fine-grained image datasets.http://www.sciencedirect.com/science/article/pii/S2667241324000156Open setImage recognitionImage clusteringDeep learningDeep neural networks |
| spellingShingle | Xuelin Zhang Feng Liu Xuelian Cheng Siyuan Yan Zhibin Liao Zongyuan Ge Autonomous novel class discovery for vision-based recognition in non-interactive environments Cognitive Robotics Open set Image recognition Image clustering Deep learning Deep neural networks |
| title | Autonomous novel class discovery for vision-based recognition in non-interactive environments |
| title_full | Autonomous novel class discovery for vision-based recognition in non-interactive environments |
| title_fullStr | Autonomous novel class discovery for vision-based recognition in non-interactive environments |
| title_full_unstemmed | Autonomous novel class discovery for vision-based recognition in non-interactive environments |
| title_short | Autonomous novel class discovery for vision-based recognition in non-interactive environments |
| title_sort | autonomous novel class discovery for vision based recognition in non interactive environments |
| topic | Open set Image recognition Image clustering Deep learning Deep neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2667241324000156 |
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