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Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning
In this paper, we propose an Interactive key-value Memory- augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions.
Robust Estimation of Nonrigid Transformation for Point Set Registration
In the second step, we estimate the transformation using a robust estimator called L 2 E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step.
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