A Glimpse into Computer Vision
Neural networks have recently had great success in significantly advancing the state of the art on challenging image classification and object detection datasets. However, this accuracy comes at a high computational cost both at training and testing time.
But what if one takes inspiration from how people recognize objects, by selectively focusing on the important parts of an image instead of processing an entire image at once? By ignoring irrelevant noisy features in an image, fewer pixels need to be processed, substantially reducing classification and detection complexity.
Last week, during #NIPS2014
), Google DeepMind presented Recurrent Models of Visual Attention
, a paper which describes an “attention-based task-driven visual processing” that is capable of extracting information from an image or video by adaptively selecting a sequence of smaller regions (glimpses
), processing only selected regions at high resolution.
Read the full paper at http://goo.gl/dEdWkk