Abstract
Semantic segmentation is a fundamental problem in computer vision that has attracted a lot of attention. Recent efforts have been devoted to network architecture innovations for efficient semantic segmentation that can run in real-time for autonomous driving and other applications. Information flow between scales is crucial because accurate segmentation needs both large context and fine detail. However, most existing approaches still rely on pretrained backbone models (e.g. ResNet on ImageNet). In this work, we propose to open up the backbone and design a simple yet effective multiscale network architecture, Bidirectional Pyramid Network (BPNet). BPNet takes the shape of a pyramid: information flows from bottom (high-resolution, small receptive field) to top (low-resolution, large receptive field), and from top to bottom, in a systematic manner, at every step of the processing. More importantly, fusion needs to be efficient; this is done through an add-and-multiply module with learned weights. We also apply a unary-pairwise attention mechanism to balance position sensitivity and context aggregation. Auxiliary loss is applied at multiple steps of the pyramid bottom. The resulting network achieves high accuracy with efficiency, without the need of pretraining. On the standard Cityscapes dataset, we achieve test mIoU 76.3 with 5.1M parameters and 36 fps (on Nvidia 2080 Ti), competitive with the state of the time real-time models. Meanwhile, our design is general and can be used to build heavier networks: a ResNet-101 equivalent version of BPNet achieves mIoU 81.9 on Cityscapes, competitive with the best published results. We further demonstrate the flexibility of BPNet on a prostate MRI segmentation task, achieving the state of the art with a 45\(\times \) speed-up.
J. Xue—Work done during internship at Amap.
Code is available at https://github.com/ginobilinie/BPNet.
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Nie, D., Xue, J., Ren, X. (2021). Bidirectional Pyramid Networks for Semantic Segmentation. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12622. Springer, Cham. https://doi.org/10.1007/978-3-030-69525-5_39
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