This paper studies the problem of RGB-D object recognition. Inspired by the great success of deep convolutional neural networks (DCNN) in AI, researchers have tried to apply it to improve the performance of RGB-D object recognition. However, DCNN always requires a large-scale annotated dataset to supervise its training. Manually labeling such a large RGB-D dataset is expensive and time consuming, which prevents DCNN from quickly promoting this research area. To address this problem, we propose a semi-supervised multimodal deep learning framework to train DCNN effectively based on very limited labeled data and massive unlabeled data. The core of our framework is a novel diversity preserving co-training algorithm, which can successfully guide DCNN to learn from the unlabeled RGB-D data by making full use of the complementary cues of the RGB and depth data in object representation. Experiments on the benchmark RGB-D dataset demonstrate that, with only 5% labeled training data, our approach achieves competitive performance for object recognition compared with those state-of-the-art results reported by fully-supervised methods.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper studies the problem of RGB-D object recognition. Inspired by the great success of deep convolutional neural networks (DCNN) in AI, researchers have tried to apply it to improve the performance of RGB-D object recognition. However, DCNN always requires a large-scale annotated dataset to supervise its training. Manually labeling such a large RGB-D dataset […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"3345-3351","msr_page_range_start":"3345","msr_page_range_end":"3351","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc. of the 25th International Joint Conference on Artificial Intelligence 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