Search Results for author: Mennatullah Siam

Found 28 papers, 11 papers with code

System Identification of Neural Systems: Going Beyond Images to Modelling Dynamics

no code implementations19 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.

Video Understanding

TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking

no code implementations13 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.

Semantic Segmentation Video Object Segmentation +1

Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation

no code implementations15 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.

Image Segmentation Medical Image Segmentation +3

Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation

1 code implementation15 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.

Segmentation Semantic Segmentation +3

Towards a Better Understanding of the Computer Vision Research Community in Africa

no code implementations11 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.

3D Reconstruction object-detection +2

A Unified Multiscale Encoder-Decoder Transformer for Video Segmentation

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.

Action Segmentation Optical Flow Estimation +6

Temporal Transductive Inference for Few-Shot Video Object Segmentation

1 code implementation27 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.

Meta-Learning Object +3

Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving

1 code implementation7 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.

Autonomous Driving Contrastive Learning +2

Video Class Agnostic Segmentation Benchmark for Autonomous Driving

1 code implementation19 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.

Autonomous Driving Instance Segmentation +3

Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings

no code implementations26 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.

Few-Shot Learning Object +5

One-Shot Weakly Supervised Video Object Segmentation

no code implementations18 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.

Object Segmentation +4

Adaptive Masked Proxies for Few-Shot Segmentation

1 code implementation19 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.

Continual Learning Few-Shot Semantic Segmentation +4

Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting

1 code implementation17 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.

Incremental Learning Robot Manipulation +4

ShuffleSeg: Real-time Semantic Segmentation Network

2 code implementations10 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.

Real-Time Semantic Segmentation Segmentation

RTSeg: Real-time Semantic Segmentation Comparative Study

2 code implementations7 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.

Autonomous Driving Benchmarking +2

4-DoF Tracking for Robot Fine Manipulation Tasks

no code implementations6 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.

Unifying Registration based Tracking: A Case Study with Structural Similarity

1 code implementation15 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.

Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks

no code implementations30 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.


Recurrent Fully Convolutional Networks for Video Segmentation

no code implementations1 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.

Change Detection Image Segmentation +4

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