论文标题
视频语义细分和失真感知功能校正
Video Semantic Segmentation with Distortion-Aware Feature Correction
论文作者
论文摘要
近年来，视频语义细分从图像语义细分的巨大进步中受益。对于这样的任务，由于高计算成本，在实践中通常无法接受。为了解决此问题，许多作品使用基于流的功能传播来重复使用以前的帧的功能。但是，光流估计不可避免地会遭受不准确性，然后导致传播特征扭曲。在本文中，我们提出了失真感知功能校正以减轻问题，从而通过纠正扭曲的传播特征来改善视频细分性能。要具体而言，我们首先建议将失真模式从特征传递到图像空间中，并进行有效的失真图预测。受益于失真图的指导，我们提出了特征校正模块（FCM）来纠正扭曲区域中的传播特征。我们提出的方法可以大大以低价提高视频语义细分的准确性。关于城市景观和Camvid的广泛实验结果表明，我们的方法的表现优于最近的最新方法。
Video semantic segmentation is active in recent years benefited from the great progress of image semantic segmentation. For such a task, the per-frame image segmentation is generally unacceptable in practice due to high computation cost. To tackle this issue, many works use the flow-based feature propagation to reuse the features of previous frames. However, the optical flow estimation inevitably suffers inaccuracy and then causes the propagated features distorted. In this paper, we propose distortion-aware feature correction to alleviate the issue, which improves video segmentation performance by correcting distorted propagated features. To be specific, we firstly propose to transfer distortion patterns from feature into image space and conduct effective distortion map prediction. Benefited from the guidance of distortion maps, we proposed Feature Correction Module (FCM) to rectify propagated features in the distorted areas. Our proposed method can significantly boost the accuracy of video semantic segmentation at a low price. The extensive experimental results on Cityscapes and CamVid show that our method outperforms the recent state-of-the-art methods.
