Analyzing the Properties of Contrastive Learning and Masked Image Modeling in Vision Transformers and Their Potential Synergy
- The study compares self-supervised learning methods Contrastive Learning (CL) and Masked Image Modeling (MIM) in Vision Transformers (ViTs).
- CL captures longer-range global patterns, focusing on object shapes and utilizing low-frequency signals, while MIM is texture-oriented and utilizes high-frequency signals.
- CL and MIM can complement each other, with a simple harmonization potentially leveraging the advantages of both methods.
A recent comparative study delves into the properties and differences between two widely used self-supervised learning methods for Vision Transformers (ViTs): Contrastive Learning (CL) and Masked Image Modeling (MIM). The study uncovers various opposing properties of the two methods, including image information, feature representations, and lead role components.
Contrastive Learning trains self-attentions to capture longer-range global patterns, focusing on the shape of objects, especially in the later layers of the ViT architecture. This property helps ViTs linearly separate images in their representation spaces but can lead to homogeneity in self-attention, reducing representation diversity and affecting scalability and dense prediction performance. In contrast, Masked Image Modeling mainly focuses on early layers and utilizes high-frequency signals, making it more texture-oriented.
The study also suggests that CL and MIM can complement each other. By harmonizing the two methods, a combined model can potentially outperform the individual methods, leveraging the advantages of both.
Future research directions include exploring better ways to combine CL and MIM objectives, extending findings on self-supervision for multi-stage ViTs, and enhancing individual properties of CL and MIM. Techniques that improve CL or MIM learning of shapes or textures, respectively, may further enhance performance. This study offers valuable insights for developing more effective self-supervised learning approaches in the realm of Vision Transformers.