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Structured Prediction with Output Embeddings for Semantic Image Annotation
2016
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
We address the task of annotating images with semantic tuples. Solving this problem requires an algorithm which is able to deal with hundreds of classes for each argument of the tuple. In such contexts, data sparsity becomes a key challenge, as there will be a large number of classes for which only a few examples are available. We propose handling this by incorporating feature representations of both the inputs (images) and outputs (argument classes) into a factorized log-linear model, and
doi:10.18653/v1/n16-1068
dblp:conf/naacl/QuattoniRMSM16
fatcat:5f2k23rpbjd3ne3b2qih4eabmy