用视觉语言模型无标注检测航天器，省去繁琐人工标记。
Annotation Free Spacecraft Detection and Segmentation using Vision Language Models
- 用预训练模型自动生成伪标签，再通过师生蒸馏训练轻量模型。
- 在三个数据集上分割精度最高提升10个点，显著优于直接零样本推理。
- 适合缺乏标注数据的太空目标检测任务，尤其适合资源受限场景。
视觉语言模型（VLM）在开放世界零样本视觉识别中表现优异，但在空间应用中的潜力尚未充分探索。由于可见度低、光照变化及目标与行星背景融合等因素，航天器的精准人工标注极为困难。因此，开发无需大量人工标注即可检测和分割航天器的方法至关重要。本文提出一种基于VLM的无标注航天目标检测与分割流程：首先利用预训练VLM自动为少量未标注真实数据生成伪标签；随后在教师-学生标签蒸馏框架中使用这些伪标签训练轻量级模型。尽管伪标签存在固有噪声，蒸馏过程仍使性能远超直接零样本VLM推理。在SPARK-2024、SPEED+和TANGO数据集上的分割任务实验表明，平均精度（AP）最高提升达10个百分点。代码与模型已公开于https://github.com/giddyyupp/annotation-free-spacecraft-segmentation。
原文摘要 · Abstract (English)
Vision Language Models (VLMs) have demonstrated remarkable performance in open-world zero-shot visual recognition. However, their potential in space-related applications remains largely unexplored. In the space domain, accurate manual annotation is particularly challenging due to factors such as low visibility, illumination variations, and object blending with planetary backgrounds. Developing methods that can detect and segment spacecraft and orbital targets without requiring extensive manual labeling is therefore of critical importance. In this work, we propose an annotation-free detection and segmentation pipeline for space targets using VLMs. Our approach begins by automatically generating pseudo-labels for a small subset of unlabeled real data with a pre-trained VLM. These pseudo-labels are then leveraged in a teacher-student label distillation framework to train lightweight models. Despite the inherent noise in the pseudo-labels, the distillation process leads to substantial performance gains over direct zero-shot VLM inference. Experimental evaluations on the SPARK-2024, SPEED+, and TANGO datasets on segmentation tasks demonstrate consistent improvements in average precision (AP) by up to 10 points. Code and models are available at https://github.com/giddyyupp/annotation-free-spacecraft-segmentation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。