Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Parts

2026-06-19

AI Summary

This work addresses the challenge of instance segmentation in low-texture, uniformly colored industrial scenes, where existing methods struggle due to insufficient texture and contextual cues, and where instance definitions often vary by task. The authors propose a boundary-aware few-shot instance segmentation framework that leverages a pretrained foundation model to extract visual features and employs a lightweight signed distance function (SDF) head to predict boundary-sensitive distance maps. Instance masks are then reconstructed from these SDF predictions. By shifting supervision from internal appearance to explicit boundary modeling, the approach enables flexible instance definition—such as whole objects or subparts—via mask conditioning. A pixel-wise shallow MLP facilitates rapid training and fine-grained control. Experiments demonstrate superior few-shot generalization, robustness, and precise control over segmentation granularity on low-texture industrial components and food imagery.

Abstract

Recent advances in large pre-trained models have led to remarkable progress in instance segmentation on general images. However, industrial scenarios remain challenging. Instance definitions are often application-specific and inconsistent, and the domain gap from general imagery is substantial due to weak textures and limited contextual cues. Consequently, a direct application of existing models is unreliable. We propose Boundary-by-Mask, a few-shot instance segmentation framework that supervises boundaries instead of interior appearance. Given a few RGB images and corresponding instance masks, the method extracts rich visual features using a foundation-model encoder and trains a lightweight Signed Distance Function (SDF) head to predict boundary-aware distance maps. Segmentation masks are obtained through an SDF-to-mask reconstruction process. By explicitly estimating contours, the framework achieves reliable instance separation even on low-texture and color-uniform surfaces. The instance definition is conditioned by the instance mask. Replacing the mask specifies the segmentation target, such as the whole object or a sub-part. A pixel-wise shallow MLP head enables rapid training. Experiments on industrial parts and food items with ambiguous boundaries show strong few-shot generalization, robustness in feature-poor conditions, and precise control over mask-level targets.

Problem

• few-shot instance segmentation
• texture-poor industrial parts
• boundary ambiguity
• domain gap
• mask-level target definition

Innovation

• few-shot instance segmentation
• mask-conditioned boundary learning
• Signed Distance Function (SDF)
• texture-poor industrial parts
• boundary-aware distance map