DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Junsik | ko |
dc.contributor.author | Lee, Seokju | ko |
dc.contributor.author | Oh, Tae-Hyun | ko |
dc.contributor.author | Kweon, In-So | ko |
dc.date.accessioned | 2018-02-21T05:20:43Z | - |
dc.date.available | 2018-02-21T05:20:43Z | - |
dc.date.created | 2017-11-29 | - |
dc.date.created | 2017-11-29 | - |
dc.date.created | 2017-11-29 | - |
dc.date.issued | 2018-02 | - |
dc.identifier.citation | 32nd AAAI Conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence Conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence, pp.6975 - 6982 | - |
dc.identifier.uri | http://hdl.handle.net/10203/239994 | - |
dc.description.abstract | Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data. However, the acquisition of a large supervised dataset is often challenging. This is also true for intelligent transportation applications, i.e., traffic sign recognition. For example, a model trained with data of one country may not be easily generalized to another country without much data. We propose a novel feature embedding scheme for unseen class classification when the representative class template is given. Traffic signs, unlike other objects, have official images. We perform co-domain embedding using a quadruple relationship from real and synthetic domains. Our quadruplet network fully utilizes the explicit pairwise similarity relationships among samples from different domains. We validate our method on three datasets with two experiments involving one-shot classification and feature generalization. The results show that the proposed method outperforms competing approaches on both seen and unseen classes. | - |
dc.language | English | - |
dc.publisher | Association for the Advancement of Artificial Intelligence (AAAI) | - |
dc.title | Co-Domain Embedding Using Deep Quadruplet Networks for Unseen Traffic Sign Recognition | - |
dc.type | Conference | - |
dc.identifier.wosid | 000485488907010 | - |
dc.identifier.scopusid | 2-s2.0-85060443685 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 6975 | - |
dc.citation.endingpage | 6982 | - |
dc.citation.publicationname | 32nd AAAI Conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence Conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Hilton New Orleans Riverside | - |
dc.contributor.localauthor | Kweon, In-So | - |
dc.contributor.nonIdAuthor | Kim, Junsik | - |
dc.contributor.nonIdAuthor | Lee, Seokju | - |
dc.contributor.nonIdAuthor | Oh, Tae-Hyun | - |
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