The Segment Anything Model (SAM), introduced by Meta AI Research as a generic object segmentation model, quickly garnered widespread attention and significantly influenced the academic community. To extend its application to video, Meta further develops Segment Anything Model 2 (SAM2), a unified model capable of both video and image segmentation. SAM2 shows notable improvements over its predecessor in terms of applicable domains, promptable segmentation accuracy, and running speed. However, this report reveals a decline in SAM2's ability to perceive different objects in images without prompts in its auto mode, compared to SAM. Specifically, we employ the challenging task of camouflaged object detection to assess this performance decrease, hoping to inspire further exploration of the SAM model family by researchers. The results of this paper are provided in \url{https://github.com/luckybird1994/SAMCOD}.

本研究解决了SAM2在无提示自动模式下对图像中不同物体的感知能力下降的问题。通过研究伪装物体检测这一挑战性任务，文章提出了一种新的评估方法，以期激发研究者对SAM模型家族的进一步探索。研究结果显示，尽管SAM2在视频和图像分割方面有显著提升，其在特定应用上的局限性仍值得关注。

评估SAM2在伪装物体检测中的作用：从SAM到SAM2