Guided Upsampling Network for Real-Time Semantic Segmentation
This work addresses the need for real-time semantic segmentation in applications like autonomous driving, offering an incremental improvement by enhancing upsampling in existing encoder-decoder architectures.
The paper tackles the problem of real-time semantic segmentation by proposing a Guided Upsampling Network (GUN) with a Guided Upsampling Module (GUM) that introduces learnable transformations for upsampling, enabling efficient processing of high-resolution images on the Cityscapes dataset with state-of-the-art performance.
Semantic segmentation architectures are mainly built upon an encoder-decoder structure. These models perform subsequent downsampling operations in the encoder. Since operations on high-resolution activation maps are computationally expensive, usually the decoder produces output segmentation maps by upsampling with parameters-free operators like bilinear or nearest-neighbor. We propose a Neural Network named Guided Upsampling Network which consists of a multiresolution architecture that jointly exploits high-resolution and large context information. Then we introduce a new module named Guided Upsampling Module (GUM) that enriches upsampling operators by introducing a learnable transformation for semantic maps. It can be plugged into any existing encoder-decoder architecture with little modifications and low additional computation cost. We show with quantitative and qualitative experiments how our network benefits from the use of GUM module. A comprehensive set of experiments on the publicly available Cityscapes dataset demonstrates that Guided Upsampling Network can efficiently process high-resolution images in real-time while attaining state-of-the art performances.