no code implementations • 1 Jun 2016 • Sepehr Valipour, Mennatullah Siam, Martin Jagersand, Nilanjan Ray
Accordingly, we propose a novel method for online segmentation of video sequences that incorporates temporal data.
no code implementations • 30 Jun 2016 • Sepehr Valipour, Mennatullah Siam, Eleni Stroulia, Martin Jagersand
Visual detection methods represent a cost-effective option, since they can take advantage of hardware usually already available in many parking lots, namely cameras.
1 code implementation • 15 Jul 2016 • Abhineet Singh, Mennatullah Siam, Martin Jagersand
This paper adapts a popular image quality measure called structural similarity for high precision registration based tracking while also introducing a simpler and faster variant of the same.
no code implementations • 16 Nov 2016 • Mennatullah Siam, Sepehr Valipour, Martin Jagersand, Nilanjan Ray
This architecture is tested for both binary and semantic video segmentation tasks.
no code implementations • 6 Mar 2017 • Mennatullah Siam, Abhineet Singh, Camilo Perez, Martin Jagersand
One of these trackers is a newly developed learning based tracker that relies on learning discriminative correlation filters while the other is a refinement of a recent 8 DoF RANSAC based tracker adapted with a new appearance model for tracking 4 DoF motion.
no code implementations • 8 Jul 2017 • Mennatullah Siam, Sara Elkerdawy, Martin Jagersand, Senthil Yogamani
In this paper, the semantic segmentation problem is explored from the perspective of automated driving.
no code implementations • 14 Sep 2017 • Mennatullah Siam, Heba Mahgoub, Mohamed Zahran, Senthil Yogamani, Martin Jagersand, Ahmad El-Sallab
Our experiments show that the proposed method outperforms state of the art methods that utilize motion cue only with 21. 5% in mAP on KITTI MOD.
2 code implementations • 7 Mar 2018 • Mennatullah Siam, Mostafa Gamal, Moemen Abdel-Razek, Senthil Yogamani, Martin Jagersand
In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods.
2 code implementations • 10 Mar 2018 • Mostafa Gamal, Mennatullah Siam, Moemen Abdel-Razek
It is shown that skip architecture in the decoding method provides the best compromise for the goal of real-time performance, while it provides adequate accuracy by utilizing higher resolution feature maps for a more accurate segmentation.
1 code implementation • 17 Oct 2018 • Mennatullah Siam, Chen Jiang, Steven Lu, Laura Petrich, Mahmoud Gamal, Mohamed Elhoseiny, Martin Jagersand
A human teacher can show potential objects of interest to the robot, which is able to self adapt to the teaching signal without providing manual segmentation labels.
1 code implementation • 19 Feb 2019 • Mennatullah Siam, Boris Oreshkin, Martin Jagersand
Our method is evaluated on PASCAL-$5^i$ dataset and outperforms the state-of-the-art in the few-shot semantic segmentation.
no code implementations • ICLR Workshop LLD 2019 • Mennatullah Siam, Boris Oreshkin
Deep learning has mainly thrived by training on large-scale datasets.
1 code implementation • ICCV 2019 • Mennatullah Siam, Boris N. Oreshkin, Martin Jagersand
Our method is evaluated on PASCAL-5^i dataset and outperforms the state-of-the-art in the few-shot semantic segmentation.
no code implementations • 18 Dec 2019 • Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin, Hengshuai Yao, Martin Jagersand
Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks.
no code implementations • 26 Jan 2020 • Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin, Hengshuai Yao, Martin Jagersand
Our results show that few-shot segmentation benefits from utilizing word embeddings, and that we are able to perform few-shot segmentation using stacked joint visual semantic processing with weak image-level labels.
no code implementations • 16 Aug 2020 • Eslam Mohamed, Mahmoud Ewaisha, Mennatullah Siam, Hazem Rashed, Senthil Yogamani, Waleed Hamdy, Muhammad Helmi, Ahmad El-Sallab
Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues.
1 code implementation • 19 Mar 2021 • Mennatullah Siam, Alex Kendall, Martin Jagersand
We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects.
1 code implementation • 7 May 2021 • Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects.
1 code implementation • 27 Mar 2022 • Mennatullah Siam, Konstantinos G. Derpanis, Richard P. Wildes
In this paper, we present a simple but effective temporal transductive inference (TTI) approach that leverages temporal consistency in the unlabelled video frames during few-shot inference.
1 code implementation • CVPR 2022 • Matthew Kowal, Mennatullah Siam, Md Amirul Islam, Neil D. B. Bruce, Richard P. Wildes, Konstantinos G. Derpanis
To show the efficacy of our approach, we analyse two widely studied tasks, action recognition and video object segmentation.
no code implementations • 3 Nov 2022 • Matthew Kowal, Mennatullah Siam, Md Amirul Islam, Neil D. B. Bruce, Richard P. Wildes, Konstantinos G. Derpanis
(ii) Some datasets that are assumed to be biased toward dynamics are actually biased toward static information.
no code implementations • CVPR 2023 • Rezaul Karim, He Zhao, Richard P. Wildes, Mennatullah Siam
In this paper, we present an end-to-end trainable unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in video.
no code implementations • 11 May 2023 • Abdul-Hakeem Omotayo, Mai Gamal, Eman Ehab, Gbetondji Dovonon, Zainab Akinjobi, Ismaila Lukman, Houcemeddine Turki, Mahmod Abdien, Idriss Tondji, Abigail Oppong, Yvan Pimi, Karim Gamal, Ro'ya-CV4Africa, Mennatullah Siam
Moreover, we study all computer vision publications beyond top-tier venues in different African regions to find that mainly Northern and Southern Africa are publishing in computer vision with 68. 5% and 15. 9% of publications, resp.
1 code implementation • 15 Jul 2023 • Mennatullah Siam, Rezaul Karim, He Zhao, Richard Wildes
We present a meta-learned Multiscale Memory Comparator (MMC) for few-shot video segmentation that combines information across scales within a transformer decoder.
no code implementations • 15 Nov 2023 • Hesham Ali, Idriss Tondji, Mennatullah Siam
In this study, we propose a novel approach to nuclei segmentation that leverages the available labelled and unlabelled data.
no code implementations • 13 Dec 2023 • Raghav Goyal, Wan-Cyuan Fan, Mennatullah Siam, Leonid Sigal
In this work we propose a novel, clip-based DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges.
no code implementations • 21 Jan 2024 • Abdul-Hakeem Omotayo, Ashery Mbilinyi, Lukman Ismaila, Houcemeddine Turki, Mahmoud Abdien, Karim Gamal, Idriss Tondji, Yvan Pimi, Naome A. Etori, Marwa M. Matar, Clifford Broni-Bediako, Abigail Oppong, Mai Gamal, Eman Ehab, Gbetondji Dovonon, Zainab Akinjobi, Daniel Ajisafe, Oluwabukola G. Adegboro, Mennatullah Siam
The aim is to provide a survey of African computer vision topics, datasets and researchers.
no code implementations • 19 Feb 2024 • Mai Gamal, Mohamed Rashad, Eman Ehab, Seif Eldawlatly, Mennatullah Siam
Towards this end, we propose a system identification study focused on comparing single image vs. video understanding models with respect to the visual cortex recordings.
no code implementations • 17 Apr 2024 • Mir Rayat Imtiaz Hossain, Mennatullah Siam, Leonid Sigal, James J. Little
These learned visual prompts are used to prompt a multiscale transformer decoder to facilitate accurate dense predictions.