VSCode-V2:用于一般视觉突出和伪装对象检测的动态提示式学习,采用双阶段优化.
IEEE transactions on pattern analysis and machine intelligence
|November 21, 2025
概括
VSCode-v2通过自适应提示和两阶段训练来增强突出物体检测 (SOD) 和伪装物体检测 (COD). 这种通用主义模型提高了性能,并实现了对新任务的零射击通用化.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 突出物体检测 (SOD) 和伪装物体检测 (COD) 是计算机视觉中的关键但不同的任务.
- 现有的方法经常使用复杂的,特定任务的架构,限制了概括.
- 之前的工作,VSCode,建立了一个使用VST和2D提示符的SOD和COD任务的通用模型.
研究的目的:
- 提高SOD和COD任务的VSCode模型的概括能力.
- 引入适应性提示和优化训练策略,以提高绩效.
- 为了使零射击概括到新的多式联运检测任务.
主要方法:
- 拟议的VSCode-v2,具有混合提示专家 (MoPE) 层,用于自适应提示生成.
- 实施了两阶段的培训过程:共享特征学习,随后是任务特定特征学习.
- 从以前的模型中整合知识蒸和一个具有数据增强的对比学习机制.
主要成果:
- 在6个SOD和COD任务中,VSCode-v2实现了平衡的性能改进.
- 该模型展示了对各种多式联络输入的有效处理.
- 在RGB-D视频SOD.等新型任务上展示了零射击概括能力.
结论:
- VSCode-v2代表了通用物体检测模型的重大进步.
- 提出的方法提高了SOD和COD的适应性和概括性.
- 该模型显示了处理多样化和未见的多式联运检测挑战的强大潜力.
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