Processing Megapixel Images with Deep Attention-Sampling Models

Understand

Existing deep architectures cannot operate on very large signals such as megapixel images due to computational and memory constraints.

• To tackle this limitation, we propose a fully differentiable end-to-end trainable model that samples and processes only a fraction of the full resolution input image.
• The locations to process are sampled from an attention distribution computed from a low resolution view of the input.
• We refer to our method as attention sampling and it can process images of several megapixels with a standard single GPU setup.