CLUSTSEG: Clustering for Universal Segmentation

• URL: http://arxiv.org/abs/2305.02187v2
• Date: Thu, 18 May 2023 15:34:53 GMT
• Title: CLUSTSEG: Clustering for Universal Segmentation
• Authors: James Liang, Tianfei Zhou, Dongfang Liu, Wenguan Wang
• Abstract summary: CLUSTSEG is a general, transformer-based framework for image segmentation. It tackles different image segmentation tasks (i.e., superpixel, semantic, instance, and panoptic) through a unified neural clustering scheme.
• Score: 56.58677563046506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present CLUSTSEG, a general, transformer-based framework that tackles different image segmentation tasks (i.e., superpixel, semantic, instance, and panoptic) through a unified neural clustering scheme. Regarding queries as cluster centers, CLUSTSEG is innovative in two aspects:1) cluster centers are initialized in heterogeneous ways so as to pointedly address task-specific demands (e.g., instance- or category-level distinctiveness), yet without modifying the architecture; and 2) pixel-cluster assignment, formalized in a cross-attention fashion, is alternated with cluster center update, yet without learning additional parameters. These innovations closely link CLUSTSEG to EM clustering and make it a transparent and powerful framework that yields superior results across the above segmentation tasks.

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