Deep Cognitive Gate: Resembling Human Cognition for Saliency Detection.

Saliency detection by human refers to the ability to identify pertinent information using our perceptive and cognitive capabilities. While human perception is attracted by visual stimuli, our cognitive capability is derived from the inspiration of constructing concepts of reasoning. Saliency detection has gained intensive interest with the aim of resembling human perceptual system. However, saliency related to human cognition, particularly the analysis of complex salient regions (cogitating process), is yet to be fully exploited. We propose to resemble human cognition, coupled with human perception, to improve saliency detection. We recognize saliency in three phases (Seeing - Perceiving - Cogitating), mimicking human's perceptive and cognitive thinking of an image. In our method, Seeing phase is related to human perception, and we formulate the Perceiving and Cogitating phases related to the human cognition systems via deep neural networks (DNNs) to construct a new module (Cognitive Gate) that enhances the DNN features for saliency detection. To the best of our knowledge, this is the first work that established DNNs to resemble human cognition for saliency detection. In our experiments, our approach outperformed 17 benchmarking DNN methods on six well-recognized datasets, demonstrating that resembling human cognition improves saliency detection.