BiDet

BiDet is a binarized neural network learning method for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. On the contrary, BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal, through which the detection precision is enhanced with alleviated false positives. Specifically, the information bottleneck (IB) principle is generalized to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized.