Abstract
Self-supervised pretraining (SSP) has emerged as a popular technique in
machine learning, enabling the extraction of meaningful feature representations
without labelled data. In the realm of computer vision, pretrained vision
transformers (ViTs) have played a pivotal role in advancing transfer learning.
Nonetheless, the escalating cost of finetuning these large models has posed a
challenge due to the explosion of model size. This study endeavours to evaluate
the effectiveness of pure self-supervised learning (SSL) techniques in computer
vision tasks, obviating the need for finetuning, with the intention of
emulating human-like capabilities in generalisation and recognition of unseen
objects. To this end, we propose an evaluation protocol for zero-shot
segmentation based on a prompting patch. Given a point on the target object as
a prompt, the algorithm calculates the similarity map between the selected
patch and other patches, upon that, a simple thresholding is applied to segment
the target. Another evaluation is intra-object and inter-object similarity to
gauge discriminatory ability of SSP ViTs. Insights from zero-shot segmentation
from prompting and discriminatory abilities of SSP led to the design of a
simple SSP approach, termed MMC. This approaches combines Masked image
modelling for encouraging similarity of local features, Momentum based
self-distillation for transferring semantics from global to local features, and
global Contrast for promoting semantics of global features, to enhance
discriminative representations of SSP ViTs. Consequently, our proposed method
significantly reduces the overlap of intra-object and inter-object
similarities, thereby facilitating effective object segmentation within an
image. Our experiments reveal that MMC delivers top-tier results in zero-shot
semantic segmentation across various datasets.