RAG-SEG( First RAG, Second SEG: A Training-Free Paradigm for Camouflaged Object Detection )

1 minute read

Published: July 29, 2025

Paper Link: https://arxiv.org/abs/2508.15313

以下展示的 SEG 结果来源于网络截图，仅用于学术交流。若涉及您的合法权益，请及时联系我们（wutaoliu@nuaa.edu.cn），我们将及时处理。
The SEG results shown below are sourced from online screenshots and are intended for academic use only. If these materials infringe on your rights, please contact us at wutaoliu at nuaa.edu.cn for prompt resolution.

感谢

以下排名不分先后：陈虹伊，《无间道》，《百家讲坛》，《原神》，《教父》，《状元苏乞儿》，谭维维，张碧晨，《甄嬛传》,张国荣, 泽连斯基，特朗普。

分布外分割的一些说明

Notes on Out-of-Distribution (OOD) Segmentation

📌 我们的方法在分布外场景下依然能够取得较好的效果，但部分场景仍存在一定噪声，后续我们将继续优化。
📌 Our method performs well even in out-of-distribution scenarios, although some noise remains in specific cases. We will continue to refine our approach.

视频展示

Video Demonstration

📌 这些视频结果直接源自我们的 Train-Free 方法，仅依赖视觉检索与分割，而没有任何针对视频序列的训练或优化。
📌 These video results are directly generated from our train-free pipeline, relying solely on visual retrieval and segmentation without any temporal training or optimization.

对比已有的一些方法（如 SAM2 中通过 label id 实现跨帧一致性），我们刻意没有引入此类技术，而是力求方案尽量简单、通用。
Unlike some existing methods (e.g., SAM2 using label IDs for cross-frame consistency), we intentionally avoided such mechanisms to keep our approach simple, and generalizable.

尽管在连续帧中可能出现一定闪烁或不稳定情况，但整体物体的定位与边界保持较好，尤其是在复杂背景和遮挡场景下，依然能够准确地聚焦目标区域。
Although some flickering or instability may occur between frames, our method still accurately locates and segments objects, especially in complex backgrounds and occlusion-heavy scenes.

我们逐帧进行推理。 We perform inference on a movie frame by frame.

🎥 示例视频 1：人物运动

🎥 Example Video 1: Human Movement

🎥 示例视频 2：电影片段 (OOD)

🎥 Example Video 2: Movie Scene (OOD)

本部分充分展示了方法在处理分布外样本方面的能力，尤其在手枪与手铐等物体的分割任务中表现突出。 This section demonstrates the strong generalization ability of our method on out-of-distribution (OOD) data, particularly in segmenting challenging objects such as handguns and handcuffs.

Image 结果展示

🌟 请注意，我们展示的结果从左到右依次为：原图、初始分割结果、后处理结果以及叠加图（Overlay）。 🌟 Please note that the results are presented from left to right in the following order: original image, initial segmentation result, post-processed result, and overlay.

Image Results

💡 虽然初始步骤中存在一定噪声，但得益于我们的后处理机制，部分干扰信息已被有效过滤，因此整体效果依然令人满意。 💡 Although some noise appears in the initial stage, our post-processing effectively filters most artifacts, resulting in satisfactory outcomes.