1 code implementation • International Conference on Machine Learning and Applications (ICMLA) 2024 • Alaa Nfissi, Wassim Bouachir, Nizar Bouguila, Brian Mishara
In speech emotion recognition (SER), using predefined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information.
1 code implementation • IEEE 18th International Conference on Semantic Computing (ICSC) 2024 • Alaa Nfissi, Wassim Bouachir, Nizar Bouguila, Brian Mishara
In light of these challenges, we present a novel end-to-end (E2E) method for speech emotion recognition (SER) as a mean of detecting changes in emotional state, that may indicate a high risk of suicide.
1 code implementation • Journal of Imaging 2024 • Sneha Paul, Zachary Patterson, Nizar Bouguila
In this study, we explore and benchmark two popular semi-supervised methods from the perspective image domain for fish-eye image segmentation.
Ranked #1 on Semi-Supervised Semantic Segmentation on WoodScape
no code implementations • International Conference on Machine Learning and Applications (ICMLA) 2023 • Sneha Paul, Zachary Patterson, Nizar Bouguila
This can be attributed to the fact that the models are not designed to handle fisheye images, and the available fisheye datasets are not sufficiently large to effectively train complex models.
no code implementations • 1 Nov 2023 • Ahmed Zgaren, Wassim Bouachir, Nizar Bouguila
One of the main problems when planning planting operations is the difficulty in estimating the number of mounds present on a planting block, as their number may greatly vary depending on site characteristics.
1 code implementation • The Visual Computer 2023 • Sneha Paul, Zachary Patterson, Nizar Bouguila
The SparseNet, a relatively larger network, samples a small number of points from the complete point cloud, while the DenseNet, a lightweight network, takes in a larger number of points as input.
Ranked #36 on 3D Point Cloud Classification on ScanObjectNN
1 code implementation • 20th Conference on Robots and Vision (CRV) 2023 • Sneha Paul, Zachary Patterson, Nizar Bouguila
In this study, we introduce a novel selfsupervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2D rendered images of the point clouds.
3D Object Classification 3D Point Cloud Linear Classification +4
no code implementations • 21st IEEE International Conference on Machine Learning and Applications (ICMLA) 2023 • Alaa Nfissi, Wassim Bouachir, Nizar Bouguila, Brian Mishara
Instead of using hand- crafted features or spectrograms, we train CNNs to recognise low-level speech representations from raw waveform, which allows the network to capture relevant narrow-band emotion characteristics.
1 code implementation • Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR) 2023 • Sneha Paul, Zachary Patterson, Nizar Bouguila
PointNet is a pioneering approach in this direction that feeds the 3D point cloud data directly to a model.
no code implementations • 6 Sep 2022 • Majid Nikougoftar Nategh, Ahmed Zgaren, Wassim Bouachir, Nizar Bouguila
Counting the number of mounds is generally conducted through manual field surveys by forestry workers, which is costly and prone to errors, especially for large areas.