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Semantic Regularisation for Recurrent Image Annotation
[article]
2016
arXiv
pre-print
The "CNN-RNN" design pattern is increasingly widely applied in a variety of image annotation tasks including multi-label classification and captioning. Existing models use the weakly semantic CNN hidden layer or its transform as the image embedding that provides the interface between the CNN and RNN. This leaves the RNN overstretched with two jobs: predicting the visual concepts and modelling their correlations for generating structured annotation output. Importantly this makes the end-to-end
arXiv:1611.05490v1
fatcat:hedw7uovwjgftp2wwi4uvaoi7y