Towards Granularity-adjusted Pixel-level Semantic Annotation

• URL: http://arxiv.org/abs/2312.02420v1
• Date: Tue, 5 Dec 2023 01:37:18 GMT
• Title: Towards Granularity-adjusted Pixel-level Semantic Annotation
• Authors: Rohit Kundu, Sudipta Paul, Rohit Lal and Amit K. Roy-Chowdhury
• Abstract summary: GranSAM provides semantic segmentation at the user-defined granularity level on unlabeled data without the need for any manual supervision. We accumulate semantic information from synthetic images generated by the Stable Diffusion model or web crawled images. We conducted experiments on the PASCAL VOC 2012 and COCO-80 datasets and observed a +17.95% and +5.17% increase in mIoU.
• Score: 26.91350707156658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in computer vision predominantly rely on learning-based systems, leveraging annotations as the driving force to develop specialized models. However, annotating pixel-level information, particularly in semantic segmentation, presents a challenging and labor-intensive task, prompting the need for autonomous processes. In this work, we propose GranSAM which distinguishes itself by providing semantic segmentation at the user-defined granularity level on unlabeled data without the need for any manual supervision, offering a unique contribution in the realm of semantic mask annotation method. Specifically, we propose an approach to enable the Segment Anything Model (SAM) with semantic recognition capability to generate pixel-level annotations for images without any manual supervision. For this, we accumulate semantic information from synthetic images generated by the Stable Diffusion model or web crawled images and employ this data to learn a mapping function between SAM mask embeddings and object class labels. As a result, SAM, enabled with granularity-adjusted mask recognition, can be used for pixel-level semantic annotation purposes. We conducted experiments on the PASCAL VOC 2012 and COCO-80 datasets and observed a +17.95% and +5.17% increase in mIoU, respectively, compared to existing state-of-the-art methods when evaluated under our problem setting.

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