Can the characteristics of different network structures be compared? Ant & meituan & NTU & Ali proposed a cross architecture self supervised video representation learning method CaCl, performance SOTA

This article shares CVPR 2022 The paper 『Cross-Architecture Self-supervised Video Representation Learning』, Raise questions ： The characteristics of different network structures can also be compared ？ And by ants & Meituan & Nanjing University & Ali proposes a cross architecture self supervised video representation learning method CACL, In the task of video retrieval and motion recognition SOTA！

Thesis link ：

https://arxiv.org/abs/2205.13313

Project links ：

https://github.com/guoshengcv/CACL

In this paper , The author proposes a new cross architecture contrastive learning for self supervised video representation learning （cross-architecture contrastive learning,CACL） frame .CACL By a 3D CNN And a video Transformer form , They are used in parallel to generate various alignments for comparative learning . This enables the model to represent Xi Qiang from these different but meaningful aspects .

Besides , The author introduces a time self - supervised learning module , The module can explicitly predict the editing distance between two video sequences in time order , This enables the model to learn rich temporal representations . The author's comments on the method in this paper UCF101 and HMDB51 The video retrieval and motion recognition tasks on the dataset are evaluated , The results show that this method has achieved excellent performance , Much more than Video MoCo and MoCo+BE And other state-of-the-art methods .