1 citations · 1 across the 4 of their papers we have counts for
4 papers
Deterministic Mode Proposals: An Efficient Alternative to Generative Sampling for Ambiguous Segmentation
Sebastian Gerard, Josephine Sullivan
Many segmentation tasks, such as medical image segmentation or future state prediction, are inherently ambiguous, meaning that multiple predictions are equally correct. Current met…
Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods
Sebastian Gerard, Josephine Sullivan
Predicting future states in uncertain environments, such as wildfire spread, medical diagnosis, or autonomous driving, requires models that can consider multiple plausible outcomes…
A simple, strong baseline for building damage detection on the xBD dataset
Sebastian Gerard, Paul Borne-Pons, Josephine Sullivan
We construct a strong baseline method for building damage detection by starting with the highly-engineered winning solution of the xView2 competition, and gradually stripping away…
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs
Sebastian Gerard, Josephine Sullivan
Domain-specific variants of contrastive learning can construct positive pairs from two distinct in-domain images, while traditional methods just augment the same image twice. For e…