Search Results for author: Mennatullah Siam

Found 21 papers, 10 papers with code

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 Semantic Segmentation +2

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 +1

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 +2

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 One-shot visual object segmentation +3

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.

Semantic Segmentation Video Object Segmentation +2

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 +3

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

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 Real-Time Semantic Segmentation

MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving

no code implementations14 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.

Autonomous Driving Motion Detection +5

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.

Management

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 +3

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