no code implementations • 17 Feb 2024 • Abuzar B. M. Adam, Mohammed A. M. Elhassan, Elhadj Moustapha Diallo
In the simulation results, we compare the performance of the proposed deep learning models and the iterative solutions in terms of accuracy and inference speed to show their suitability for the real-time application in next generation networks.
no code implementations • 23 Oct 2023 • Mohammed A. M. Elhassan, Changjun Zhou, Amina Benabid, Abuzar B. M. Adam
In particular, our P2AT variants achieve state-of-art results on the Camvid dataset 80. 5%, 81. 0%, 81. 1% for P2AT-S, P2ATM, and P2AT-L, respectively.
1 code implementation • 15 Jun 2022 • Mohammed A. M. Elhassan, Chenhui Yang, Chenxi Huang, Tewodros Legesse Munea, Xin Hong, Abuzar B. M. Adam, Amina Benabid
This paper presents a new model to achieve a trade-off between accuracy/speed for real-time road scene semantic segmentation.
Ranked #1 on Real-Time Semantic Segmentation on Cityscapes
1 code implementation • 4 Apr 2022 • Mohammed A. M. Elhassan, Chenhui Yang, Chenxi Huang, Tewodros Legesse Munea
The following is a technical report to test the validity of the proposed Subspace Pyramid Fusion Module (SPFM) to capture multi-scale feature representations, which is more useful for semantic segmentation.