activity
20232026
most citedImage Generation Diversity Issues and How to Tame Them

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2026

Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits

John Bonnici, Matthew Baugh, Aleksandra Kulbaka +3

Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function, yet manual selection of these key frames…

cs.CV2024★ 1 cited

Image Generation Diversity Issues and How to Tame Them

Mischa Dombrowski, Weitong Zhang, Sarah Cechnicka +2

Generative methods now produce outputs nearly indistinguishable from real data but often fail to fully capture the data distribution. Unlike quality issues, diversity limitations i…

cs.CV2024

JVID: Joint Video-Image Diffusion for Visual-Quality and Temporal-Consistency in Video Generation

Hadrien Reynaud, Matthew Baugh, Mischa Dombrowski +3

We introduce the Joint Video-Image Diffusion model (JVID), a novel approach to generating high-quality and temporally coherent videos. We achieve this by integrating two diffusion…

cs.CV2024

Data-Efficient Generation for Dataset Distillation

Zhe Li, Weitong Zhang, Sarah Cechnicka +1

While deep learning techniques have proven successful in image-related tasks, the exponentially increased data storage and computation costs become a significant challenge. Dataset…

cs.CV2023

Realistic Data Enrichment for Robust Image Segmentation in Histopathology

Sarah Cechnicka, James Ball, Hadrien Reynaud +3

Poor performance of quantitative analysis in histopathological Whole Slide Images (WSI) has been a significant obstacle in clinical practice. Annotating large-scale WSIs manually i…