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From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic Segmentation
[article]
2022
arXiv
pre-print
Zero-shot learning has been actively studied for image classification task to relieve the burden of annotating image labels. Interestingly, semantic segmentation task requires more labor-intensive pixel-wise annotation, but zero-shot semantic segmentation has only attracted limited research interest. Thus, we focus on zero-shot semantic segmentation, which aims to segment unseen objects with only category-level semantic representations provided for unseen categories. In this paper, we propose a
arXiv:2009.12232v4
fatcat:gcjhzj42cbaklh5ebq3pzszn6q