mlsanityData to 14 Oct 2026

PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning

arXiv 2308.12757 ↗First posted 24 Aug 2023cs.CVcited by 3

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Abstract (arXiv)

In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that leveraging the textual space of a powerful pre-trained image-language model (such as CLIP) can be beneficial in learning visual features. Therefore, we develop a novel method termed PartSeg for few-shot part segmentation based on multimodal learning. Specifically, we design a part-aware prompt learning method to generate part-specific prompts that enable the CLIP model to better understand the concept of ``part'' and fully utilize its textual space. Furthermore, since the concept of the same part under different object categories is general, we establish relationships between these parts during the prompt learning process. We conduct extensive experiments on the PartImageNet and Pascal_Part datasets, and the experimental results demonstrated that our proposed method achieves state-of-the-art performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 44 of its 64 references that are in the map. First posted on arXiv 24 Aug 2023.

Builds on

44 of 64 references.

References · 44

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32 more
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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Image Segmentation in Foundation Model Era: A Surveycited by 35
  2. 2024No Re-Train, More Gain: Upgrading Backbones with Diffusion model for Pixel-Wise and Weakly-Supervised Few-Shot Segmentationcited by 0
  3. 2025A Comparative Study of Vision Transformers and CNNs for Few-Shot Rigid Transformation and Fundamental Matrix Estimationcited by 0