ZS-VCOS：基于光流和开放词汇目标检测的零样本视频伪装目标分割
ZS-VCOS: Zero-Shot Video Camouflaged Object Segmentation By Optical Flow and Open Vocabulary Object Detection
AI总结：
本文提出ZS-VCOS，通过将SAM-2、Owl-v2与时序信息集成到模块化流程中实现零样本视频伪装目标分割，在MoCA-Mask上F-measure从0.296提升至0.628，并超越有监督方法。
摘要：
与传统分割任务相比，伪装目标分割面临独特挑战，主要原因是伪装目标与其背景在纹理和颜色上高度相似。有效解决这一问题对害虫防治、缺陷检测以及医学影像中的病灶分割等关键领域具有重要意义。此前的研究主要侧重于有监督或无监督的预训练方法，零样本方法的发展明显不足。现有的零样本技术通常以自动模式使用Segment Anything Model（SAM），或依赖视觉-语言模型生成分割提示；然而，由于伪装目标与背景的相似性，其性能仍不理想。本文研究如何通过将SAM-2和Owl-v2等大型预训练模型与时序信息集成到一个模块化流程中，从而避免训练。在MoCA-Mask数据集上的评估表明，我们的方法取得了显著的性能提升，将F-measure（$F_β^w$）从0.296提高到0.628，大幅优于现有零样本方法。我们的方法还超越了有监督方法，将F-measure从0.476提高到0.628。此外，在MoCA-Filter数据集上的评估显示，与有监督迁移方法FlowSAM相比，成功率从0.628提高到0.697。全面的消融研究进一步验证了每个组件的单独贡献。除主要贡献外，我们还指出了先前工作在指标和设置上的不一致之处。代码见https://github.com/weathon/vcos。
英文摘要：
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, due to the similarity of the camouflaged object and the background. This work studies how to avoid training by integrating large pre-trained models like SAM-2 and Owl-v2 with temporal information into a modular pipeline. Evaluated on the MoCA-Mask dataset, our approach achieves outstanding performance improvements, significantly outperforming existing zero-shot methods by raising the F-measure ($F_β^w$) from 0.296 to 0.628. 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. Besides our main contributions, we also highlight inconsistencies in previous work regarding metrics and settings. Code can be found in https://github.com/weathon/vcos.
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。
