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[TLC16a] Cross-Modal Classification by Completing Unimodal Representations
Conférence Internationale avec comité de lecture : Workshop Vision and Language Integration Meets Multimedia Fusion, with ACM Multimedia, October 2016, pp. 17-25, Amsterdam, NL, (DOI: 10.1145/2983563.2983570)
motcle:
Résumé:
We argue that cross-modal classification, where models are trained on data from one modality (e.g. text) and applied to data from another (e.g. image), is a relevant problem in multimedia retrieval. We propose a method that addresses this specific problem, related to but different from cross-modal retrieval and bimodal classification. This method relies on a common latent space where both modalities have comparable representations and on an auxiliary dataset from which we build a more complete bimodal representation of any unimodal data. Evaluations on Pascal VOC07 and NUS-WIDE show that the novel representation method significantly improves the results compared to the use of a latent space alone. The level of performance achieved makes cross-modal classification a convincing choice for real applications.
Collaboration:
CEA
BibTeX
| @inproceedings { | |||
| TLC16a, | |||
| title | = | "{Cross-Modal Classification by Completing Unimodal Representations}", | |
| author | = | " T. Tran and H. Le Borgne and M. Crucianu ", | |
| booktitle | = | "{Workshop Vision and Language Integration Meets Multimedia Fusion, with ACM Multimedia}", | |
| year | = | 2016, | |
| month | = | "October", | |
| pages | = | " 17-25", | |
| address | = | "Amsterdam, NL", | |
| doi | = | "10.1145/2983563.2983570", | |
| } | |||
