Search Results for author:
Found 14 papers, 11 papers with code
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial.
Scalable Label-efficient Footpath Network Generation Using Remote Sensing Data and Self-supervised Learning
The annotation of segmentation tasks, especially labeling remote sensing images with specialized requirements, is very expensive, so we aim to introduce a pipeline requiring less labeled data.
Undercover Deepfakes: Detecting Fake Segments in Videos
This paradigm has been under-explored by the current deepfake detection methods in the academic literature.
Semantic Segmentation using Vision Transformers: A survey
Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis.
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning
Inspired by the abundance of publicly available remote sensing projects and the burgeoning development of deep learning in computer vision, our research focuses on assessing fire risk using remote sensing imagery.
Ranked #1 on
Remote Sensing Image Classification
on FireRisk
DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops
Due to the costly nature of remote sensing image labeling and the large volume of available unlabeled imagery, self-supervised methods that can learn feature representations without manual annotation have received great attention.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet-10%
(using extra training data)
IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)
This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C).
Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer vision
Analysis of overhead imagery using computer vision is a problem that has received considerable attention in academic literature.
Self-Supervised Vision Transformers for Malware Detection
Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks.
Ranked #1 on
Malware Detection
on MalNet
(F1 score metric)
DALLE-URBAN: Capturing the urban design expertise of large text to image transformers
However, a detailed analysis capturing the capabilities of such models, specifically with a focus on the built environment, has not been performed to date.
Self-Supervision, Remote Sensing and Abstraction: Representation Learning Across 3 Million Locations
However, the performance of such methods on remote sensing imagery domains remains under-explored.
Does a Face Mask Protect my Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images
Contactless and efficient systems are implemented rapidly to advocate preventive methods in the fight against the COVID-19 pandemic.
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning
The specialized weights trained by our method outperform standard face recognition features for masked to unmasked face matching.
