Abstract
Camouflaged object segmentation presents unique challenges compared to traditional segmentation tasks, primarily due to the high similarity in patterns and colors between camouflaged objects and their backgrounds. Effective solutions to this problem have significant implications in critical areas such as pest control, defect detection, and lesion segmentation in medical imaging. Prior research has predominantly emphasized supervised or unsupervised pre-training methods, leaving zero-shot approaches significantly underdeveloped. Existing zero-shot techniques commonly utilize the Segment Anything Model (SAM) in automatic mode or rely on vision-language models to generate cues for segmentation; however, their performances remain unsatisfactory, likely due to the similarity of the camouflaged object and the background. Optical flow, commonly utilized for detecting moving objects, has demonstrated effectiveness even with camouflaged entities. Our method integrates optical flow, a vision-language model, and SAM 2 into a sequential pipeline. Evaluated on the MoCA-Mask dataset, our approach achieves outstanding performance improvements, significantly outperforming existing zero-shot methods by raising the F-measure ($F_\beta^w$) from 0.296 to 0.628. Remarkably, our approach also surpasses supervised methods, increasing the F-measure from 0.476 to 0.628. Additionally, evaluation on the MoCA-Filter dataset demonstrates an increase in the success rate from 0.628 to 0.697 when compared with FlowSAM, a supervised transfer method. A thorough ablation study further validates the individual contributions of each component. More details can be found on this https URL.
Abstract (translated)
伪装物体分割相较于传统的分割任务提出了独特的挑战，主要是由于伪装对象与其背景在图案和颜色上的高度相似性。该问题的有效解决方案对害虫控制、缺陷检测以及医学影像中的病变分割等关键领域具有重要意义。先前的研究主要集中在监督学习或无监督预训练方法上，而零样本（zero-shot）方法的发展相对滞后。现有的零样本技术通常利用Segment Anything Model (SAM) 的自动模式或者依赖视觉-语言模型来生成用于分割的提示；然而，由于伪装对象与背景的高度相似性，这些方法的表现不尽如人意。 光学流（optical flow），常被用来检测移动物体，在处理伪装实体时也表现出有效性。我们提出的方法结合了光学流、视觉-语言模型和SAM 2，并将其整合为一个顺序管道。在MoCA-Mask数据集上进行的评估显示，我们的方法实现了显著的性能提升，将F-measure（$F_\beta^w$）从0.296提高到0.628，大幅超越了现有的零样本方法。值得注意的是，这种方法还超过了监督学习的方法，提高了F-measure从0.476至0.628。此外，在MoCA-Filter数据集上的评估表明，相较于FlowSAM（一种监督式迁移学习方法），成功概率从0.628提高到了0.697。 详细的消融实验进一步验证了每个组成部分的独立贡献。有关此工作的更多详细信息，请参阅[链接]提供的资料。(注：实际链接未提供，在描述中提到的位置放置“https URL”仅为指示性表述)
URL
https://arxiv.org/abs/2505.01431
