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Papers/Joint Calibration for Semantic Segmentation

Joint Calibration for Semantic Segmentation

Holger Caesar, Jasper Uijlings, Vittorio Ferrari

2015-07-06SegmentationSemantic Segmentation
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Abstract

Semantic segmentation is the task of assigning a class-label to each pixel in an image. We propose a region-based semantic segmentation framework which handles both full and weak supervision, and addresses three common problems: (1) Objects occur at multiple scales and therefore we should use regions at multiple scales. However, these regions are overlapping which creates conflicting class predictions at the pixel-level. (2) Class frequencies are highly imbalanced in realistic datasets. (3) Each pixel can only be assigned to a single class, which creates competition between classes. We address all three problems with a joint calibration method which optimizes a multi-class loss defined over the final pixel-level output labeling, as opposed to simply region classification. Our method outperforms the state-of-the-art on the popular SIFT Flow [18] dataset in both the fully and weakly supervised setting by a considerably margin (+6% and +10%, respectively).

Results

TaskDatasetMetricValueModel
Semantic SegmentationSIFT-flowMean Accuracy59.2JCSS
Semantic SegmentationSIFT-flowMean Accuracy44.8JCSS (weakly supervised)
10-shot image generationSIFT-flowMean Accuracy59.2JCSS
10-shot image generationSIFT-flowMean Accuracy44.8JCSS (weakly supervised)

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