No manual prior is required! HKU & Tongji & lunarai & Kuangshi proposed self supervised visual representation learning based on semantic grouping, which significantly improved the tasks of target dete

This article shares papers 『Self-Supervised Visual Representation Learning with Semantic Grouping』, No manual priors are required ！ HKU & Tongji &LunarAI& Open vision （ Zhangxiangyu team ） Self supervised visual representation learning based on semantic grouping is proposed , Significantly improve target detection 、 Instance segmentation and semantic segmentation tasks ！

Thesis link ：

https://arxiv.org/abs/2205.15288

Abstract

In this paper , The author solves the problem of learning visual representation from unlabeled scene centric data . Existing work has demonstrated the potential of utilizing complex underlying structures in scenario centric data ; For all that , They usually rely on hand-made object priors or special excuse tasks to build learning frameworks , This could undermine universality .

contrary , The author proposes to carry out contrastive learning from data-driven semantic slots , namely SlotCon, For joint semantic grouping and representation learning . Semantic grouping is performed by assigning pixels to a set of learnable prototypes , These prototypes can be adapted to each sample by concentrating features , And form new grooves （slot）. Slots based on the learned data , Use comparative goals to express learning , Enhanced feature resolution , This in turn facilitates the grouping of semantically related pixels .

Compared with previous work , By optimizing semantic grouping and contrastive learning at the same time , The method in this paper bypasses the shortcoming of manual prior , Be able to learn objects from scene centric images / Group level representation . Experiments show that , This method can effectively decompose complex scenes into semantic groups for feature learning , And for downstream tasks （ Including target detection 、 Instance segmentation and semantic segmentation ） It's obviously helpful .