Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs can not fully exploit the global temporal and spatial information in successive video frames, resulting in false-positive segmentation results. In this paper, we propose the novel PNS-Net (Progressively Normalized Self-attention Network), which can efficiently learn representations from polyp videos with real-time speed (~140fps) on a single RTX 2080 GPU and no post-processing. Our PNS-Net is based solely on a basic normalized self-attention block, equipping with recurrence and CNNs entirely. Experiments on challenging VPS datasets demonstrate that the proposed PNS-Net achieves state-of-the-art performance. We also conduct extensive experiments to study the effectiveness of the channel split, soft-attention, and progressive learning strategy. We find that our PNS-Net works well under different settings, making it a promising solution to the VPS task.

本文提出了一种名为PNS-Net的新型进展性规范化自注意力网络，其中采用归一化的自注意力块，结合卷积神经网络和重复网络，能够在单个RTX 2080 GPU上实现真实时间速度（约为140fps），且无需后处理。实验表明PNS-Net在挑战性的视频息肉分割数据集上表现出了最先进的性能，具有不同设置下工作效果很好的潜力，成为处理视频息肉分割任务的有前途的解决方案。

视频息肉分割的逐渐归一化自注意力网络