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  1. Proceedings of the 2016 ACM workshop on Vision and Language Integration Meets Multimedia Fusion (iV&L-MM '16)
  2. Cross-modal Classification by Completing Unimodal Representations
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Exploiting Scene Context for Image Captioning
News Event Understanding by Mining Latent Factors From Multimodal Tensors
Jointly Representing Images and Text: Dependency Graphs, Word Senses, and Multimodal Embeddings
Multimodal and Crossmodal Representation Learning from Textual and Visual Features with Bidirectional Deep Neural Networks for Video Hyperlinking
Beyond Language and Vision, Towards Truly Multimedia Integration
Cross-modal Classification by Completing Unimodal Representations
User Video Summarization Based on Joint Visual and Semantic Affinity Graph
Semantic Indexing of Wearable Camera Images: Kids'Cam Concepts
Disinformation in Multimedia Annotation: Misleading Metadata Detection on YouTube

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Cross-modal Classification by Completing Unimodal Representations

Content Provider ACM Digital Library
Author Tran, Thi Quynh Nhi Crucianu, Michel Le Borgne, Hervé
Abstract 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.
Starting Page 17
Ending Page 25
Page Count 9
File Format PDF
ISBN 9781450345194
DOI 10.1145/2983563.2983570
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-16
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Latent representations Unimodal data completion Kernel canonical correlation analysis
Content Type Text
Resource Type Article
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