FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution

03/09/2020
by   Zhanpeng Zhang, et al.
0

Real-time semantic segmentation is desirable in many robotic applications with limited computation resources. One challenge of semantic segmentation is to deal with the object scale variations and leverage the context. How to perform multi-scale context aggregation within limited computation budget is important. In this paper, firstly, we introduce a novel and efficient module called Cascaded Factorized Atrous Spatial Pyramid Pooling (CF-ASPP). It is a lightweight cascaded structure for Convolutional Neural Networks (CNNs) to efficiently leverage context information. On the other hand, for runtime efficiency, state-of-the-art methods will quickly decrease the spatial size of the inputs or feature maps in the early network stages. The final high-resolution result is usually obtained by non-parametric up-sampling operation (e.g. bilinear interpolation). Differently, we rethink this pipeline and treat it as a super-resolution process. We use optimized super-resolution operation in the up-sampling step and improve the accuracy, especially in sub-sampled input image scenario for real-time applications. By fusing the above two improvements, our methods provide better latency-accuracy trade-off than the other state-of-the-art methods. In particular, we achieve 68.4 at 84 fps on the Cityscapes test set with a single Nivida Titan X (Maxwell) GPU card. The proposed module can be plugged into any feature extraction CNN and benefits from the CNN structure development.

READ FULL TEXT

page 1

page 2

page 4

page 6

research
02/23/2023

Efficient Context Integration through Factorized Pyramidal Learning for Ultra-Lightweight Semantic Segmentation

Semantic segmentation is a pixel-level prediction task to classify each ...
research
06/08/2021

CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation

Real-time semantic segmentation has received considerable attention due ...
research
09/17/2019

Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network

This paper introduces a novel method to super-resolve multi-spectral ima...
research
08/05/2020

OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network

Super-resolution (SR) has achieved great success due to the development ...
research
07/26/2019

Context-Integrated and Feature-Refined Network for Lightweight Urban Scene Parsing

Semantic segmentation for lightweight urban scene parsing is a very chal...
research
12/16/2022

Atrous Space Bender U-Net (ASBU-Net/LogiNet)

With recent advances in CNNs, exceptional improvements have been made in...
research
06/07/2021

Multi-Exit Semantic Segmentation Networks

Semantic segmentation arises as the backbone of many vision systems, spa...

Please sign up or login with your details

Forgot password? Click here to reset