One-shot In-context Part Segmentation

• URL: http://arxiv.org/abs/2503.01144v1
• Date: Mon, 03 Mar 2025 03:50:54 GMT
• Title: One-shot In-context Part Segmentation
• Authors: Zhenqi Dai, Ting Liu, Xingxing Zhang, Yunchao Wei, Yanning Zhang,
• Abstract summary: We present the One-shot In-context Part (OIParts) framework to tackle the challenges of part segmentation.<n>Our framework offers a novel approach to part segmentation that is training-free, flexible, and data-efficient.<n>We have achieved remarkable segmentation performance across diverse object categories.
• Score: 97.77292483684877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present the One-shot In-context Part Segmentation (OIParts) framework, designed to tackle the challenges of part segmentation by leveraging visual foundation models (VFMs). Existing training-based one-shot part segmentation methods that utilize VFMs encounter difficulties when faced with scenarios where the one-shot image and test image exhibit significant variance in appearance and perspective, or when the object in the test image is partially visible. We argue that training on the one-shot example often leads to overfitting, thereby compromising the model's generalization capability. Our framework offers a novel approach to part segmentation that is training-free, flexible, and data-efficient, requiring only a single in-context example for precise segmentation with superior generalization ability. By thoroughly exploring the complementary strengths of VFMs, specifically DINOv2 and Stable Diffusion, we introduce an adaptive channel selection approach by minimizing the intra-class distance for better exploiting these two features, thereby enhancing the discriminatory power of the extracted features for the fine-grained parts. We have achieved remarkable segmentation performance across diverse object categories. The OIParts framework not only eliminates the need for extensive labeled data but also demonstrates superior generalization ability. Through comprehensive experimentation on three benchmark datasets, we have demonstrated the superiority of our proposed method over existing part segmentation approaches in one-shot settings.

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