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Found 11 papers, 5 papers with code
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.
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting
Since water systems are interconnected and the connectivity information between the stations is implicit, the proposed model leverages a graph learning module to extract a sparse graph adjacency matrix adaptively based on the data.
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
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.
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks.
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories
While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory data, a multi-task learning approach does not bring any considerable advantage over single-task learners.
A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis
In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches.
Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey
In our final model, we combine the output of CNN models using "average voting", "majority voting" and "optimal weights" methods.
Semi-supervised GANs to Infer Travel Modes in GPS Trajectories
Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data.
