2018

CRAM: Clued Recurrent Attention Model

Chung, Minki, Cho, Sungzoon

Understand

To overcome the poor scalability of convolutional neural network, recurrent attention model(RAM) selectively choose what and where to look on the image.

By directing recurrent attention model how to look the image, RAM can be even more successful in that the given clue narrow down the scope of the possible focus zone.
In this perspective, this work proposes clued recurrent attention model (CRAM) which add clue or constraint on the RAM better problem solving.
CRAM follows encoder-decoder framework, encoder utilizes recurrent attention model with spatial transformer network and decoder which varies depending on the task.

Built on

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Then

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2025
Closest in time.

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