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Self-supervised Semantic Segmentation Grounded in Visual Concepts
In this work, we propose a self-supervised pixel representation learning method for semantic segmentation by using visual concepts (i. e., groups of pixels with semantic meanings, such as parts, objects, and scenes) extracted from images.
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