Search Results for author:
Found 9 papers, 5 papers with code
ShapeFusion: A 3D diffusion model for localized shape editing
In the realm of 3D computer vision, parametric models have emerged as a ground-breaking methodology for the creation of realistic and expressive 3D avatars.
Locally Adaptive Neural 3D Morphable Models
We present the Locally Adaptive Morphable Model (LAMM), a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes.
Rethinking the Domain Gap in Near-infrared Face Recognition
Heterogeneous face recognition (HFR) involves the intricate task of matching face images across the visual domains of visible (VIS) and near-infrared (NIR).
ViTs for SITS: Vision Transformers for Satellite Image Time Series
In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT).
Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification
In training machine learning models for land cover semantic segmentation there is a stark contrast between the availability of satellite imagery to be used as inputs and ground truth data to enable supervised learning.
DeepSatData: Building large scale datasets of satellite images for training machine learning models
This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models with emphasis placed on dense classification tasks, e. g. semantic segmentation.
Context-self contrastive pretraining for crop type semantic segmentation
For crop type semantic segmentation from Satellite Image Time Series (SITS) we find performance at parcel boundaries to be a critical bottleneck and explain how CSCL tackles the underlying cause of that problem, improving the state-of-the-art performance in this task.
Extracting deep local features to detect manipulated images of human faces
Recent developments in computer vision and machine learning have made it possible to create realistic manipulated videos of human faces, raising the issue of ensuring adequate protection against the malevolent effects unlocked by such capabilities.
Synthesising 3D Facial Motion from "In-the-Wild" Speech
Synthesising 3D facial motion from speech is a crucial problem manifesting in a multitude of applications such as computer games and movies.
