Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples

• URL: http://arxiv.org/abs/2112.03185v1
• Date: Mon, 6 Dec 2021 17:32:38 GMT
• Title: Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples
• Authors: Nir Zabari, Yedid Hoshen
• Abstract summary: We propose a novel approach for creating semantic segmentation masks for every object. Our method takes as input the image-level labels of the class categories present in the image. The output of this stage provides pixel-level pseudo-labels, instead of the manual pixel-level labels required by supervised methods.
• Score: 34.97652735163338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation is a key computer vision task that has been actively researched for decades. In recent years, supervised methods have reached unprecedented accuracy, however they require many pixel-level annotations for every new class category which is very time-consuming and expensive. Additionally, the ability of current semantic segmentation networks to handle a large number of categories is limited. That means that images containing rare class categories are unlikely to be well segmented by current methods. In this paper we propose a novel approach for creating semantic segmentation masks for every object, without the need for training segmentation networks or seeing any segmentation masks. Our method takes as input the image-level labels of the class categories present in the image; they can be obtained automatically or manually. We utilize a vision-language embedding model (specifically CLIP) to create a rough segmentation map for each class, using model interpretability methods. We refine the maps using a test-time augmentation technique. The output of this stage provides pixel-level pseudo-labels, instead of the manual pixel-level labels required by supervised methods. Given the pseudo-labels, we utilize single-image segmentation techniques to obtain high-quality output segmentation masks. Our method is shown quantitatively and qualitatively to outperform methods that use a similar amount of supervision. Our results are particularly remarkable for images containing rare categories.

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