BiDet: An Efficient Binarized Object Detector
This work addresses the need for efficient object detection in resource-constrained environments by improving binary neural networks, though it is incremental as it builds on existing binarization methods.
The paper tackled the problem of performance degradation in binarized neural networks for object detection due to information redundancy, and proposed BiDet, which uses redundancy removal to enhance detection precision and reduce false positives, achieving state-of-the-art results on PASCAL VOC and COCO datasets.
In this paper, we propose a binarized neural network learning method called BiDet 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, our 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, we generalize the information bottleneck (IB) principle 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. Meanwhile, we learn sparse object priors so that the posteriors are concentrated on informative detection prediction with false positive elimination. Extensive experiments on the PASCAL VOC and COCO datasets show that our method outperforms the state-of-the-art binary neural networks by a sizable margin.