No time to train! Training-Free Reference-Based Instance Segmentation

• URL: http://arxiv.org/abs/2507.02798v2
• Date: Sat, 05 Jul 2025 08:15:07 GMT
• Title: No time to train! Training-Free Reference-Based Instance Segmentation
• Authors: Miguel Espinosa, Chenhongyi Yang, Linus Ericsson, Steven McDonagh, Elliot J. Crowley,
• Abstract summary: This work investigates the task of object segmentation when provided with only a small set of reference images.<n>Our key insight is to leverage strong semantic priors, as learned by foundation models, to identify corresponding regions between a reference and a target image.<n>We find that correspondences enable automatic generation of instance-level segmentation masks for downstream tasks and instantiate our ideas via a multi-stage, training-free method.
• Score: 15.061599989448867
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviates this original problem through a promptable, semantics-agnostic, segmentation paradigm and yet still requires manual visual-prompts or complex domain-dependent prompt-generation rules to process a new image. Towards reducing this new burden, our work investigates the task of object segmentation when provided with, alternatively, only a small set of reference images. Our key insight is to leverage strong semantic priors, as learned by foundation models, to identify corresponding regions between a reference and a target image. We find that correspondences enable automatic generation of instance-level segmentation masks for downstream tasks and instantiate our ideas via a multi-stage, training-free method incorporating (1) memory bank construction; (2) representation aggregation and (3) semantic-aware feature matching. Our experiments show significant improvements on segmentation metrics, leading to state-of-the-art performance on COCO FSOD (36.8% nAP), PASCAL VOC Few-Shot (71.2% nAP50) and outperforming existing training-free approaches on the Cross-Domain FSOD benchmark (22.4% nAP).

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