Search Results for author:
Found 8 papers, 4 papers with code
A Semantic Segmentation-guided Approach for Ground-to-Aerial Image Matching
Nowadays the accurate geo-localization of ground-view images has an important role across domains as diverse as journalism, forensics analysis, transports, and Earth Observation.
Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images
In this paper, we develop a new model for semantic segmentation of unlabelled images, the Non-annotated Earth Observation Semantic Segmentation (NEOS) model.
PhilEO Bench: Evaluating Geo-Spatial Foundation Models
Massive amounts of unlabelled data are captured by Earth Observation (EO) satellites, with the Sentinel-2 constellation generating 1. 6 TB of data daily.
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
By including our boundary, FROB reduces the threshold linked to the model's few-shot robustness; it maintains the OoD performance approximately independent of the number of few-shots.
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
OMASGAN addresses the rarity of anomalies by generating strong and adversarial OoD samples on the distribution boundary using only normal class data, effectively addressing mode collapse.
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection
We propose a self-supervised learning few-shot confidence boundary methodology based on generative and discriminative models, including classification.
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation
In this paper, we create a GAN-based tail formation model for anomaly detection, the Tail of distribution GAN (TailGAN), to generate samples on the tail of the data distribution and detect anomalies near the support boundary.
Generative Adversarial Network
Unsupervised Anomaly Detection
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
We propose an invertible-residual-network-based model, the Boundary of Distribution Support Generator (BDSG).
