SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos
Journal:
arXiv
Published Date:
May 1, 2025
Abstract
Existing saliency detection methods struggle in real-world scenarios due to
motion blur and occlusions. In contrast, spike cameras, with their high
temporal resolution, significantly enhance visual saliency maps. However, the
composite noise inherent to spike camera imaging introduces discontinuities in
saliency detection. Low-quality samples further distort model predictions,
leading to saliency bias. To address these challenges, we propose
Spike-navigated Optimal TrAnsport Saliency Region Detection (SOTA), a framework
that leverages the strengths of spike cameras while mitigating biases in both
spatial and temporal dimensions. Our method introduces Spike-based Micro-debias
(SM) to capture subtle frame-to-frame variations and preserve critical details,
even under minimal scene or lighting changes. Additionally, Spike-based
Global-debias (SG) refines predictions by reducing inconsistencies across
diverse conditions. Extensive experiments on real and synthetic datasets
demonstrate that SOTA outperforms existing methods by eliminating composite
noise bias. Our code and dataset will be released at
https://github.com/lwxfight/sota.
