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Perception of Wildfires
Contour Refinement using Discrete Diffusion in Low Data Regime
Fei Yu Guan, Ian Keefe, Sophie Wilkinson, Daniel D.B. Perrakis, Steven Waslander
Conference on Robotics and Vision (CRV), 2026
In this work, we present a lightweight discrete diffusion contour refinement pipeline for robust boundary detection in the low data regime. We use a Convolutional Neural Network(CNN) architecture with self-attention layers as the core of our pipeline, and condition on a segmentation mask, iteratively denoising a sparse contour representation. We introduce multiple novel adaptations for improved low-data efficacy and inference efficiency, including using a simplified diffusion process, a customized model architecture, and minimal post processing to produce a dense, isolated contour given a dataset of size <500 training images.
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