5 papers
APIC: Amortized Physics-Informed Calibration using Neural Processes
Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Joachim Denzler
Physics models are inherently imperfect due to misspecified or missing mechanisms, resulting in systematic discrepancies between model predictions and real-world observations. The…
Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction
Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Adithya Ashok Chalain Valapil +1
Coherent anti-Stokes Raman scattering (CARS) spectroscopy is a powerful and rapid technique widely used in medicine, material science, and chemical analyses. However, its effective…
Saccadic Vision for Fine-Grained Visual Classification
Johann Schmidt, Sebastian Stober, Joachim Denzler +1
Fine-grained visual classification (FGVC) requires distinguishing between visually similar categories through subtle, localized features - a task that remains challenging due to hi…
Distance-informed Neural Processes
Aishwarya Venkataramanan, Joachim Denzler
We propose the Distance-informed Neural Process (DNP), a novel variant of Neural Processes that improves uncertainty estimation by combining global and distance-aware local latent…
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models
Aishwarya Venkataramanan, Paul Bodesheim, Joachim Denzler
Vision-Language Models (VLMs) learn joint representations by mapping images and text into a shared latent space. However, recent research highlights that deterministic embeddings f…