no code implementations • 27 Feb 2024 • Ashkan Taghipour, Morteza Ghahremani, Mohammed Bennamoun, Aref Miri Rekavandi, Hamid Laga, Farid Boussaid
To address these deficiencies, we introduce the Box-it-to-Bind-it (B2B) module - a novel, training-free approach for improving spatial control and semantic accuracy in text-to-image (T2I) diffusion models.
1 code implementation • 6 Aug 2023 • Lian Xu, Mohammed Bennamoun, Farid Boussaid, Hamid Laga, Wanli Ouyang, Dan Xu
Building upon the observation that the attended regions of the one-class token in the standard vision transformer can contribute to a class-agnostic localization map, we explore the potential of the transformer model to capture class-specific attention for class-discriminative object localization by learning multiple class tokens.
Object Localization Weakly supervised Semantic Segmentation +1
no code implementations • ICCV 2023 • Zhiheng Fu, Longguang Wang, Lian Xu, Zhiyong Wang, Hamid Laga, Yulan Guo, Farid Boussaid, Mohammed Bennamoun
In this paper, we thus propose an unsupervised viewpoint representation learning scheme for 3D point cloud completion without explicit viewpoint estimation.
no code implementations • 17 Sep 2022 • Laurent Jospin, Allen Antony, Lian Xu, Hamid Laga, Farid Boussaid, Mohammed Bennamoun
In this paper, we propose the Active-Passive SimStereo dataset and a corresponding benchmark to evaluate the performance gap between passive and active stereo images for stereo matching algorithms.
no code implementations • 12 Sep 2022 • Laurent Valentin Jospin, Hamid Laga, Farid Boussaid, Mohammed Bennamoun
A major focus of recent developments in stereo vision has been on how to obtain accurate dense disparity maps in passive stereo vision.
1 code implementation • 2 Dec 2021 • Laurent Valentin Jospin, Farid Boussaid, Hamid Laga, Mohammed Bennamoun
In this paper, we show that closed form formulae for subpixel disparity computation for the case of one dimensional matching, e. g., in the case of rectified stereo images where the search space is of one dimension, exists when using the standard NCC, SSD and SAD cost functions.
no code implementations • 17 Oct 2021 • Guan Wang, Hamid Laga, Anuj Srivastava
We demonstrate the utility of this framework in comparing, matching, and computing geodesics between biological objects such as neurons and botanical trees.
no code implementations • 24 Sep 2021 • Tarek Ben Charrada, Hedi Tabia, Aladine Chetouani, Hamid Laga
It is composed of of (1) a Vertex Generation Network (VGN), which predicts the initial 3D locations of the object's vertices from an input RGB image, (2) a differentiable triangulation layer, which learns in a non-supervised manner, using a novel reinforcement learning algorithm, the best triangulation of the object's vertices, and finally, (3) a hierarchical mesh refinement network that uses graph convolutions to refine the initial mesh.
1 code implementation • 2 Mar 2021 • A S M Mahmudul Hasan, Ferdous Sohel, Dean Diepeveen, Hamid Laga, Michael G. K. Jones
The rapid advances in Deep Learning (DL) techniques have enabled rapid detection, localisation, and recognition of objects from images or videos.
no code implementations • 23 Jan 2021 • Hamid Laga, Marcel Padilla, Ian H. Jermyn, Sebastian Kurtek, Mohammed Bennamoun, Anuj Srivastava
With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold.
1 code implementation • 16 Sep 2020 • A. Nugaliyadde, Kok Wai Wong, Jeremy Parry, Ferdous Sohel, Hamid Laga, Upeka V. Somaratne, Chris Yeomans, Orchid Foster
We used 60 WSIs for training the RCNN model and another 12 WSIs for testing.
4 code implementations • 14 Jul 2020 • Laurent Valentin Jospin, Wray Buntine, Farid Boussaid, Hamid Laga, Mohammed Bennamoun
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems.
no code implementations • 1 Jun 2020 • Hamid Laga, Laurent Valentin Jospin, Farid Boussaid, Mohammed Bennamoun
Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted growing interest from the community, with more than 150 papers published in this area between 2014 and 2019.
Ranked #1 on Monocular Depth Estimation on Make3D (RMSE metric)
1 code implementation • 4 Oct 2019 • Zhe Su, Martin Bauer, Stephen C. Preston, Hamid Laga, Eric Klassen
In this article we introduce a family of elastic metrics on the space of parametrized surfaces in 3D space using a corresponding family of metrics on the space of vector valued one-forms.
Differential Geometry Optimization and Control 49Q10, 58B20
no code implementations • 22 Jul 2019 • Isaac Ronald Ward, Hamid Laga, Mohammed Bennamoun
Deep learning techniques, coupled with the availability of large training datasets, have now revolutionized the field of computer vision, including RGB-D object detection, achieving an unprecedented level of performance.
no code implementations • 15 Jun 2019 • Xian-Feng Han, Hamid Laga, Mohammed Bennamoun
Given this new era of rapid evolution, this article provides a comprehensive survey of the recent developments in this field.
no code implementations • 14 Jun 2019 • Hamid Laga
Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities.
no code implementations • 25 Dec 2018 • Hamid Laga
In this chapter, we focus on recent techniques that treated the shape of 3D objects as points in some high dimensional space where paths describe deformations.
3 code implementations • 6 Oct 2018 • Md. Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin, Hamid Laga
We discuss the foundation of the techniques to analyze their performances, strengths and limitations.
no code implementations • 14 Oct 2016 • Hamid Laga, Qian Xie, Ian H. Jermyn, Anuj Srivastava
Recent developments in elastic shape analysis (ESA) are motivated by the fact that it provides comprehensive frameworks for simultaneous registration, deformation, and comparison of shapes.
no code implementations • CVPR 2014 • Hedi Tabia, Hamid Laga, David Picard, Philippe-Henri Gosselin
We evaluate the performance of the proposed Bag of Covariance Matrices framework on 3D shape matching and retrieval applications and demonstrate its superiority compared to descriptor-based techniques.