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Found 3 papers, 0 papers with code
The Making and Breaking of Camouflage
We also incorporate the proposed camouflage score into a generative model as an auxiliary loss and show that effective camouflage images or videos can be synthesised in a scalable manner.
Self-supervised Video Object Segmentation by Motion Grouping
We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models.
Ranked #7 on
Unsupervised Object Segmentation
on DAVIS 2016
Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation
We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differentiable registration module to align consecutive frames based on the background, which effectively emphasises the object boundary in the difference image, and a motion segmentation module with memory that discovers the moving objects, while maintaining the object permanence even when motion is absent at some point.
