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Found 13 papers, 2 papers with code
Anchored Diffusion for Video Face Reenactment
Furthermore, during inference, we leverage the transformer architecture to modify the diffusion process, generating a batch of non-uniform sequences anchored to a common frame, ensuring consistency regardless of temporal distance.
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models
In this paper, we employ information-theory tools to investigate this phenomenon, revealing a fundamental tradeoff between uncertainty and perception.
Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models
Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades.
On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models
The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech.
Early Time Classification with Accumulated Accuracy Gap Control
This data-driven rule attains finite-sample, distribution-free control of the accuracy gap between full and early-time classification.
Weakly-Supervised Surgical Phase Recognition
A key element of computer-assisted surgery systems is phase recognition of surgical videos.
Self-Supervised Learning for Endoscopic Video Analysis
To fully exploit the power of SSL, we create sizable unlabeled endoscopic video datasets for training MSNs.
Ranked #2 on
Surgical phase recognition
on Cholec80
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems
Our approach is agnostic to the underlying image-to-image network, and only requires triples of the input (degraded), reconstructed and true images for training.
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems
In this work, we introduce image denoisers derived as the gradients of smooth scalar-valued deep neural networks, acting as potentials.
Regularization by Denoising via Fixed-Point Projection (RED-PRO)
Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED).
Sparse Array Design via Fractal Geometries
To that end, we introduce a fractal array design in which a generator array is recursively expanded according to its difference coarray.
Deep learning in ultrasound imaging
We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications.
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.
