DC-SAM:通过双重一致性在图像和视频中的任何内容进行上下文细分
IEEE transactions on pattern analysis and machine intelligence
|December 22, 2025
概括
双一致性SAM (DC-SAM) 适应了SegmentAnything模型,用于一次性上下文细分. 这种方法通过视觉提示和交叉注意力来增强提示功能,在图像和视频基准上取得了强的结果.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 在上下文细分,少数镜头学习的一个子集,需要从单个标记的例子细分对象.
- 现有的任何细分模型 (SAM) 在交互式细分方面表现出色,但并不直接适用于上下文细分任务.
- 适应SAM用于上下文细分对于推进计算机视觉中的概括能力至关重要.
研究的目的:
- 提出双一致性SAM (DC-SAM),一种用于图像和视频的上下文细分的新方法.
- 通过使用高质量的视觉提示,在SAM中增强提示编码器的功能表示.
- 引入第一个用于上下文视频对象分割 (IC-VOS) 的基准.
主要方法:
- DC-SAM采用快速调整来适应SAM和SAM2进行上下文细分.
- 关键技术包括融合SAM特征以获得更好的提示对齐和循环一致的交叉注意力以获得特征视觉提示一致性.
- 使用具有歧视性提示的双分支设计和面具管培训策略.
主要成果:
- 在COCO-2i数据集上DC-SAM实现了55.5mIoU,在PASCAL-5i数据集上达到73mIoU.
- 该方法在新策划的IC-VOS基准指标上获得了71.52的J&F得分.
- 实验验证实了DC-SAM在图像和视频上下文细分方面的有效性.
结论:
- 在图像和视频中,DC-SAM成功地调整了SAM以实现有效的上下文细分.
- 拟议的方法在标准的少数拍摄细分基准和新型IC-VOS数据集上表现出卓越的性能.
- DC-SAM在少数拍摄学习和视觉细分领域取得了重大进展.
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