无需标注的空间目标检测与分割使用视觉语言模型
Annotation Free Spacecraft Detection and Segmentation using Vision Language Models
AI总结：
本文提出了一种基于视觉语言模型的无标注空间目标检测与分割方法，通过伪标签蒸馏提升性能，实现在多个数据集上的精度提升。
摘要：
视觉语言模型（VLMs）在开放世界零样本视觉识别中展现了卓越的性能。然而，它们在空间相关应用中的潜力仍 largely 未被探索。在空间领域，由于低能见度、光照变化和目标与行星背景融合等因素，准确的手动标注尤其具有挑战性。因此，开发无需大量手动标注即可检测和分割航天器和轨道目标的方法至关重要。在本工作中，我们提出了一种使用VLMs进行空间目标检测和分割的无标注管道。我们的方法首先利用预训练的VLM自动为一小部分未标注的真实数据生成伪标签。这些伪标签随后在教师-学生标签蒸馏框架中用于训练轻量级模型。尽管伪标签中固有噪声，蒸馏过程仍使直接零样本VLM推理的性能显著提升。在SPARK-2024、SPEED+和TANGO数据集上的分割任务实验表明，平均精度（AP）的提升可达10个百分点。代码和模型可在https://github.com/giddyyupp/annotation-free-spacecraft-segmentation上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
