Local Memory Attention for Fast Video Semantic Segmentation

Matthieu Paul; Martin Danelljan; Luc Van Gool; Radu Timofte

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arXiv · 2101.01715

Local Memory Attention for Fast Video Semantic Segmentation

Matthieu Paul·Martin Danelljan·Luc Van Gool·Radu Timofte

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Abstract

We propose a novel neural network module that transforms an existing single-frame semantic segmentation model into a video semantic segmentation pipeline. In contrast to prior works, we strive towards a simple, fast, and general module that can be integrated into virtually any single-frame architecture. Our approach aggregates a rich representation of the semantic information in past frames into a memory module. Information stored in the memory is then accessed through an attention mechanism. In contrast to previous memory-based approaches, we propose a fast local attention layer, providing temporal appearance cues in the local region of prior frames. We further fuse these cues with an encoding of the current frame through a second attention-based module. The segmentation decoder processes the fused representation to predict the final semantic segmentation. We integrate our approach into two popular semantic segmentation networks: ERFNet and PSPNet. We observe an improvement in segmentation performance on Cityscapes by 1.7% and 2.1% in mIoU respectively, while increasing inference time of ERFNet by only 1.5ms.

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Matthieu Paul, Martin Danelljan, Luc Van Gool, Radu Timofte. 2021-01-05. Local Memory Attention for Fast Video Semantic Segmentation. https://arxiv.org/abs/2101.01715

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