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Found 3 papers, 3 papers with code
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential.
Robustness and Overfitting Behavior of Implicit Background Models
In this paper, we examine the overfitting behavior of image classification models modified with Implicit Background Estimation (SCrIBE), which transforms them into weakly supervised segmentation models that provide spatial domain visualizations without affecting performance.
Implicit Background Estimation for Semantic Segmentation
Scene understanding and semantic segmentation are at the core of many computer vision tasks, many of which, involve interacting with humans in potentially dangerous ways.
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