6 papers
Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
Valeria Pais, Malena Mendilaharzu, Daniele Faccio +3
Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven: long-tailed or unbalanced…
Autoguided Online Data Curation for Diffusion Model Training
Valeria Pais, Luis Oala, Daniele Faccio +1
The costs of generative model compute rekindled promises and hopes for efficient data curation. In this work, we investigate whether recently developed autoguidance and online data…
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution
Athanasios Tragakis, Chaitanya Kaul, Kevin J. Mitchell +3
Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) dep…
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models
Athanasios Tragakis, Marco Aversa, Chaitanya Kaul +2
In this work, we introduce Pixelsmith, a zero-shot text-to-image generative framework to sample images at higher resolutions with a single GPU. We are the first to show that it is…
AI-Enabled sensor fusion of time of flight imaging and mmwave for concealed metal detection
Chaitanya Kaul, Kevin J. Mitchell, Khaled Kassem +6
In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich the information obtained from a dynamic scene. One application of this sensor…
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
Athanasios Tragakis, Qianying Liu, Chaitanya Kaul +5
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance. We re…