SILC: Improving Vision Language Pretraining with Self-Distillation
Authors: name: Muhammad Ferjad Naeem, name: Yongqin Xian, name: Xiaohua Zhai, name: Lukas Hoyer, name: Luc Van Gool, name: Federico Tombari

Image-Text pretraining on web-scale image caption dataset has become the
default recipe for open vocabulary classification and retrieval models thanks
to the success of CLIP and its variants. Several works have also used CLIP
features for dense prediction tasks and have shown the emergence of open-set
abilities. However, the contrastive objective only focuses on image-text
alignment and does not incentivise image feature learning for dense prediction
tasks. In this work, we propose the simple addition of local-to-global
correspondence learning by self-distillation as an additional objective for
contrastive pre-training to propose SILC. We show that distilling local image
features from an exponential moving average (EMA) teacher model significantly
improves model performance on several computer vision tasks including
classification, retrieval, and especially segmentation. We further show that
SILC scales better with the same training duration compared to the baselines.
Our model SILC sets a new state of the art for zero-shot classification, few
shot classification, image and text retrieval, zero-shot segmentation, and open
vocabulary segmentation.