DC Field | Value | Language |
---|---|---|
dc.contributor.author | Baddar, Wissam J | ko |
dc.contributor.author | Ro, Yong Man | ko |
dc.date.accessioned | 2018-02-21T05:20:40Z | - |
dc.date.available | 2018-02-21T05:20:40Z | - |
dc.date.created | 2017-11-17 | - |
dc.date.created | 2017-11-17 | - |
dc.date.created | 2017-11-17 | - |
dc.date.issued | 2018-02-07 | - |
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.6666 - 6673 | - |
dc.identifier.uri | http://hdl.handle.net/10203/239992 | - |
dc.description.abstract | Spatio-temporal feature encoding is essential for encoding facial expression dynamics in video sequences. At test time, most spatio-temporal encoding methods assume that a temporally segmented sequence is fed to a learned model, which could require the prediction to wait until the full sequence is available to an auxiliary task that performs the temporal segmentation. This causes a delay in predicting the expression. In an interactive setting, such as affective interactive agents, such delay in the prediction could not be tolerated. Therefore, training a model that can accurately predict the facial expression "on-the-fly" (as they are fed to the system) is essential. In this paper, we propose a new spatio-temporal feature learning method, which would allow prediction with partial sequences. As such, the prediction could be performed on-thefly. The proposed method utilizes an estimated expression intensity to generate dense labels, which are used to regulate the prediction model training with a novel objective function. As results, the learned spatio-temporal features can robustly predict the expression with partial (incomplete) expression sequences, on-the-fly. Experimental results showed that the proposed method achieved higher recognition rates compared to the state-of-the-art methods on both datasets. More importantly, the results verified that the proposed method improved the prediction frames with partial expression sequence inputs. | - |
dc.language | English | - |
dc.publisher | Association for the Advancement of Artificial Intelligence (AAAI) | - |
dc.title | Learning Spatio-temporal Features with Partial Expression Sequences for on-the-Fly Prediction | - |
dc.type | Conference | - |
dc.identifier.wosid | 000485488906092 | - |
dc.identifier.scopusid | 2-s2.0-85060472251 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 6666 | - |
dc.citation.endingpage | 6673 | - |
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 | Ro, Yong Man | - |
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