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集群2 前:用于视频实例分割的半监督集群变压器

Áron Fóthi¹, Adrián Szlatincsán¹, Ellák Somfai^1,2

¹Department of Artificial Intelligence, ELTE Eötvös Loránd University, 1053 Budapest, Hungary.

Sensors (Basel, Switzerland)

|February 10, 2024

概括

此摘要是机器生成的。

本研究介绍了Cluster2Former,这是一种用于视频实例细分的半监督学习方法. 它使用最小的涂注释来实现竞争性结果,降低数据标签成本.

关键词:

实例细分实例细分实例细分半监督学习半监督学习变压器变压器视频处理视频处理

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科学领域:

计算机科学计算机科学
人工智能的人工智能
机器学习机器学习

背景情况:

视频实例细分对于理解动态场景至关重要.
传统的方法需要广泛的像素级注释,这是昂贵和耗时的.
半监督学习为减少注释负担提供了一个潜在的解决方案.

研究的目的:

为视频实例细分开发一种新的半监督方法.
通过使用轻量级注释,减少对完全注释的数据集的依赖.
为了提高视频实例细分的成本效益和效率.

主要方法:

提出了Cluster2Former模型,增强了像Mask2Former这样的现有架构.
雇佣了基于涂的注释用于培训.
引入基于相似性的约束损失,以有效处理部分注释.

主要成果:

在标准视频实例细分基准上取得了竞争性表现.
以0.5%的注释像素来证明有效性.
展示了模型处理有限注释资源的能力.

结论:

Cluster2Former为视频实例细分提供了可行和高效的解决方案.
这种方法显著降低了注释成本和计算要求.
对于具有稀缺标记数据的应用程序来说,它特别有利.