How to effectively approximate real-valued parameters with binary codes plays a central role in neural network binarization. In this work, we reveal an important fact that binarizing different layers has a widely varied effect on the compression ratio of network and the loss of performance. Based on this fact, we propose a novel and flexible neural network binarization method by introducing the concept of layer-wise priority which binarizes parameters in inverse order of their layer depth. In each training step, our method selects a specific network layer, minimizes the discrepancy between the original real-valued weights and its binary approximations following block coordinate descent scheme. During the iteration of the above process, it is significant that we can flexibly decide whether to binarize the remaining floating layers or not and explore a trade-off between the loss of performance and the compression ratio of model. The resulting binary network is applied for efficient pedestrian detection. Experimental results on several benchmarks show that under the same compression ratio, the model compressed by our method achieves much lower miss rate and faster detection speed than those obtained by the state-of-the-art neural network binarization method.

Scale analysis plays a vital role in pedestrian detection. Conventional approaches usually directly concatenate multi-scale outputs, which is only capable of modeling first-order dependency among various scales. In contrast, this work proposes a novel scale-context modeling scheme by exploiting the highly nonlinear dependency among scales. The proposed scheme aggregates output response maps from mid-results of convolutional layers via a bi-directional recurrent sub-network. Therefore scale information could flow among different layers and implicit underlying dependency structure information in the scale space would be disclosed, which yields more consistency detection. Experimental results on Caltech Pedestrian detection benchmark demonstrate the superior detection (state-of-the-art miss rate of 8.56%) of the proposed method over prior art.