Recently, dense contrastive learning has shown superior performance on dense prediction tasks compared to instance-level contrastive learning. Despite its supremacy, the properties of dense contrastive representations have not yet been carefully studied. Therefore, we analyze the theoretical ideas of dense contrastive learning using a standard CNN and straightforward feature matching scheme rather than propose a new complex method. Inspired by the analysis of the properties of instance-level contrastive representations through the lens of alignment and uniformity on the hypersphere, we employ and extend the same lens for the dense contrastive representations to analyze their underexplored properties. We discover the core principle in constructing a positive pair of dense features and empirically proved its validity. Also, we introduces a new scalar metric that summarizes the correlation between alignment-and-uniformity and downstream performance. Using this metric, we study various facets of densely learned contrastive representations such as how the correlation changes over single- and multi-object datasets or linear evaluation and dense prediction tasks. The source code is publicly available at: https://github.com/SuperSupermoon/DenseCL-analysis
翻译:最近,密集对比性学习显示,在密集的预测任务方面,与实验级对比性学习相比,高密度对比性学习表现表现优异。尽管它具有优势,但密集对比性表现的特性尚未得到仔细研究。因此,我们利用标准的CNN和直截了当的特征匹配方案分析密集对比性学习的理论思想,而不是提出新的复杂方法。在通过高视镜的校正和统一透镜对实例级对比性表现特性进行分析的启发下,我们使用和扩展密集对比性表现的相同镜头,以分析其探索不足的特性。我们发现在构建一对正对密集特征的核心原则,并用经验证明了其有效性。此外,我们引入了一个新的标度指标,概括了对准性和统一性与下游性之间的相关性。我们利用这一指标,研究了密集学习的对比性表现的各个方面,例如单项和多项对象数据集或线性评估和密集性预测任务的相关性变化如何。源代码公布在https://github.com/SuperSupermoon/DencL-assimication。