  Self-supervised pre-training strategies have recently shown impressive
results for training general-purpose feature extraction backbones in computer
vision. In combination with the Vision Transformer architecture, the DINO
self-distillation technique has interesting emerging properties, such as
unsupervised clustering in the latent space and semantic correspondences of the
produced features without using explicit human-annotated labels. The STEGO
method for unsupervised semantic segmentation contrastively distills feature
correspondences of a DINO-pre-trained Vision Transformer and recently set a new
state of the art. However, the detailed workings of STEGO have yet to be
disentangled, preventing its usage in safety-critical applications. This paper
provides a deeper understanding of the STEGO architecture and training strategy
by conducting studies that uncover the working mechanisms behind STEGO,
reproduce and extend its experimental validation, and investigate the ability
of STEGO to transfer to different datasets. Results demonstrate that the STEGO
architecture can be interpreted as a semantics-preserving dimensionality
reduction technique.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning