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To mimic human vision with the way of recognizing the diverse and open world,foundation vision models are much critical. While recent techniques ofself-supervised learning show the promising potentiality of this mission, weargue that signals from labelled data are also important for common-senserecognition, and properly chosen pre-text tasks can facilitate the efficiencyof vision representation learning. To this end, we propose a novel pre-trainingframework by adopting both self-supervised and supervised visual pre-text tasksin a multi-task manner. Specifically, given an image, we take a heuristic wayby considering its intrinsic style properties, inside objects with theirlocations and correlations, and how it looks like in 3D space for basic visualunderstanding. However, large-scale object bounding boxes and correlations areusually hard to achieve. Alternatively, we develop a hybrid method byleveraging both multi-label classification and self-supervised learning. On theone hand, under the multi-label supervision, the pre-trained model can explorethe detailed information of an image, e.g., image types, objects, and part ofsemantic relations. On the other hand, self-supervised learning tasks, withrespect to Masked Image Modeling (MIM) and contrastive learning, can help themodel learn pixel details and patch correlations. Results show that ourpre-trained models can deliver results on par with or better thanstate-of-the-art (SOTA) results on multiple visual tasks. For example, with avanilla Swin-B backbone, we achieve 85.3\% top-1 accuracy on ImageNet-1Kclassification, 47.9 box AP on COCO object detection for Mask R-CNN, and 50.6mIoU on ADE-20K semantic segmentation when using Upernet. The performance showsthe ability of our vision foundation model to serve general purpose visiontasks.

Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning