Search Results for author:
Found 9 papers, 2 papers with code
We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
The experimental results demonstrate that, firstly, the transformed disparity (or inverse depth) images become more informative; secondly, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection; and finally, the training set augmentation technique based on adversarial domain adaptation not only improves the accuracy of the state-of-the-art semantic segmentation networks, but also accelerates their convergence.
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
ATG-PVD: Ticketing Parking Violations on A Drone
In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD).
Self-Supervised WiFi-Based Activity Recognition
Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities.
Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection
This further reduces the semantic gap between different feature channel layers.
Multimodal sensor fusion in the latent representation space
In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks.
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition
We first propose the Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion.
Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks
This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture.
Privacy in Multimodal Federated Human Activity Recognition
This work studies the impact of privacy in federated HAR at a user, environment, and sensor level.
