DC Field | Value | Language |
---|---|---|
dc.contributor.author | Baek, Kyungjune | ko |
dc.contributor.author | Lee, Minhyun | ko |
dc.contributor.author | Shim, Hyunjung | ko |
dc.date.accessioned | 2022-08-26T07:00:27Z | - |
dc.date.available | 2022-08-26T07:00:27Z | - |
dc.date.created | 2022-07-07 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.citation | 34th AAAI Conference on Artificial Intelligence, AAAI 2020, pp.10451 - 10459 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298142 | - |
dc.description.abstract | Existing co-localization techniques significantly lose performance over weakly or fully supervised methods in accuracy and inference time. In this paper, we overcome common drawbacks of co-localization techniques by utilizing self-supervised learning approach. The major technical contributions of the proposed method are two-fold. 1) We devise a new geometric transformation, namely point symmetric transformation and utilize its parameters as an artificial label for self-supervised learning. This new transformation can also play the role of region-drop based regularization. 2) We suggest a heat map extraction method for computing the heat map from the network trained by self-supervision, namely class-agnostic activation mapping. It is done by computing the spatial attention map. Based on extensive evaluations, we observe that the proposed method records new state-of-the-art performance in three fine-grained datasets for unsupervised object localization. Moreover, we show that the idea of the proposed method can be adopted in a modified manner to solve the weakly supervised object localization task. As a result, we outperform the current state-of-the-art technique in weakly supervised object localization by a significant gap. | - |
dc.language | English | - |
dc.publisher | AAAI press | - |
dc.title | PsyNet: Self-supervised approach to object localization using point symmetric transformation | - |
dc.type | Conference | - |
dc.identifier.wosid | 000668126802108 | - |
dc.identifier.scopusid | 2-s2.0-85106428856 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 10451 | - |
dc.citation.endingpage | 10459 | - |
dc.citation.publicationname | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | New York | - |
dc.identifier.doi | 10.1609/aaai.v34i07.6615 | - |
dc.contributor.localauthor | Shim, Hyunjung | - |
dc.contributor.nonIdAuthor | Baek, Kyungjune | - |
dc.contributor.nonIdAuthor | Lee, Minhyun | - |
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