Bio-inspired multi-level interactive contour detection network

Abstract

Encoding-decoding convolutional neural network structures have shown powerful performance in contour detection tasks, however, the design of models in the past seems to have little reference to physiological mechanisms, especially biological visual mechanisms. And physiological studies have shown that the visual system is efficient and accurate in the extraction of contour features. Meanwhile, when designing network models, researchers pay more attention to the impact of decoding networks on the performance of the models and pay little attention to the impact of encoding networks on the performance of the models. Therefore, in this paper, inspired by the effective mechanism of the biological vision system for contour detection, we combine it with convolutional neural net and self-attention mechanism to design a multi-level interaction contour detection model. We call it MI-Net. Among them, the multi-level interaction module is designed to simulate the transmission mechanism of visual information across the visual cortex and the feedback mechanism of visual information to realize the multi-level interaction of different levels of the encoding network, and then optimize each visual level to achieve the improvement of the performance of contour detection. The feature integration module is designed by simulating the feature integration function of the inferior temporal (IT) cortex. Multiple feature integration modules are combined into a decoding network to increase the feature integration capability of the decoding network. Experiments on publicly available datasets show that our proposed contour detection network has good performance.