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Found 11 papers, 7 papers with code
Iterative Feature Boosting for Explainable Speech Emotion Recognition
In speech emotion recognition (SER), using predefined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information.
Unlocking the Emotional States of High-Risk Suicide Callers through Speech Analysis
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.
Iterative Feature Boosting for Explainable Speech Emotion Recognition
In speech emotion recognition (SER), using pre- defined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information.
FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images
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
Semantic Segmentation Using Transfer Learning on Fisheye Images
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.
Automatic counting of planting microsites via local visual detection and global count estimation
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.
DualMLP: a two-stream fusion model for 3D point cloud classification
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
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
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
CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks
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.
Improved Training for 3D Point Cloud Classification
PointNet is a pioneering approach in this direction that feeds the 3D point cloud data directly to a model.
Automatic counting of mounds on UAV images: combining instance segmentation and patch-level correction
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.
