Universal Segmentation at Arbitrary Granularity with Language Instruction
This addresses the need for versatile segmentation models that can adapt to new scenarios without expensive retraining, though it appears incremental as it builds on prior unification attempts.
The paper tackles the problem of segmentation models being limited to specific tasks and data distributions by introducing UniLSeg, a universal segmentation model that uses language instructions to segment at any semantic level, achieving excellent performance across various tasks and surpassing specialist and unified models.
This paper aims to achieve universal segmentation of arbitrary semantic level. Despite significant progress in recent years, specialist segmentation approaches are limited to specific tasks and data distribution. Retraining a new model for adaptation to new scenarios or settings takes expensive computation and time cost, which raises the demand for versatile and universal segmentation model that can cater to various granularity. Although some attempts have been made for unifying different segmentation tasks or generalization to various scenarios, limitations in the definition of paradigms and input-output spaces make it difficult for them to achieve accurate understanding of content at arbitrary granularity. To this end, we present UniLSeg, a universal segmentation model that can perform segmentation at any semantic level with the guidance of language instructions. For training UniLSeg, we reorganize a group of tasks from original diverse distributions into a unified data format, where images with texts describing segmentation targets as input and corresponding masks are output. Combined with a automatic annotation engine for utilizing numerous unlabeled data, UniLSeg achieves excellent performance on various tasks and settings, surpassing both specialist and unified segmentation models.