分析复杂视频变换对SAM2分割性能的影响，揭示其逐级去噪与目标聚焦机制。
An Analysis of Data Transformation Effects on Segment Anything 2
- 通过复杂视频变换测试SAM2各阶段表现，观察噪声过滤与目标增强过程。
- 各阶段逐步消除干扰，提升对遮挡和杂乱场景中目标的分割精度。
- 适合关注视频分割鲁棒性与模型可解释性的研究人员参考。
视频对象分割（VOS）是视频感知与理解的关键任务。Meta AI发布的端到端视频分割最先进模型Segment-Anything Model 2（SAM 2）在干净视频数据与增强数据上均表现优异。为深入理解该架构为何能实现高质量结果，本文对一系列复杂视频变换输入进行了系统测试，并量化分析其在每个处理阶段的影响。结果显示，随着模型逐级推进，能有效过滤复杂变换带来的噪声，并强化目标物体特征。研究贡献包括构建复杂变换视频数据集、解析SAM2各阶段对变换的响应机制，以及可视化各阶段的分割输出。该分析有助于提升视频分割在真实复杂场景下的适用性与性能可追踪性，尤其在存在遮挡与背景杂乱时仍能精准定位与分割目标。
原文摘要 · Abstract (English)
Video object segmentation (VOS) is a critical task in the development of video perception and understanding. The Segment-Anything Model 2 (SAM 2), released by Meta AI, is the current state-of-the-art architecture for end-to-end VOS. SAM 2 performs very well on both clean video data and augmented data, and completely intelligent video perception requires an understanding of how this architecture is capable of achieving such quality results. To better understand how each step within the SAM 2 architecture permits high-quality video segmentation, a variety of complex video transformations are passed through the architecture, and the impact at each stage of the process is measured. It is observed that each progressive stage enables the filtering of complex transformation noise and the emphasis of the object of interest. Contributions include the creation of complex transformation video datasets, an analysis of how each stage of the SAM 2 architecture interprets these transformations, and visualizations of segmented objects through each stage. By better understanding how each model structure impacts overall video understanding, VOS development can work to improve real-world applicability and performance tracking, localizing, and segmenting objects despite complex cluttered scenes and obscurations.
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。