The KITTI Vision Benchmark Suite

Multi-encoder Context Aggregation Network (MCANet)
is a novel real-time semantic scene segmentation
model which offers the best combination of low
model complexity and state-of-the-art (SOTA)
performance on benchmark datasets. With successive
sub=encoder, it processes the feature maps
multiple times and with a novel Local and Global
Context Aggregation (LGCA) module at decoder side,
it accurately localizes the objects by fusing the
global and local spatial details.

2.7 million

@ARTICLE{10164083,
author={Singha, Tanmay and Pham, Duc-Son and
Krishna, Aneesh},
journal={IEEE Access},
title={Multi-encoder Context Aggregation Network
for Structured and Unstructured Urban Street Scene
Analysis},
year={2023},
volume={},
number={},
pages={1-1},
doi={10.1109/ACCESS.2023.3289968}}