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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

I love computer vision 2022-06-23 23:29:39 阅读数:0 评论数:0 点赞数:0 收藏数:0

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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!

The details are as follows :

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  • Thesis link :https://arxiv.org/abs/2205.13313

  • Project links :https://github.com/guoshengcv/CACL

      01      

Abstract

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 .

      02      

Motivation

Video representation learning is a basic task of video understanding , Because it plays an important role in various tasks , For example, action recognition 、 Video Retrieval . Recent work has focused on improving the performance of deep neural networks by using supervised learning , This usually requires a large-scale video dataset with very expensive human annotations , Such as Sports1M、Dynamics、HACS and MultiSports. The huge annotation cost inevitably limits the potential of deep network in learning video representation . therefore , It is important to improve this task with unlabeled video that is easy to access on a large scale .

In recent years , Self supervised learning has made great progress in learning strong image representation . It has also been extended to the field of video , Contrastive learning has been widely used in the field of video . for example , In recent work , Introducing contrast learning to capture the differences between two video instances , This enables contrastive learning to learn the representation in each video instance . However , In these methods , Contrastive learning mainly focuses on learning the global space-time representation of video , It is difficult to capture meaningful time details , These details usually provide important clues for distinguishing different video instances . therefore , Different from learning image representation , Modeling time information is very important for video representation . In this work , A new self supervised video representation method is proposed , This method can simultaneously perform video level contrast learning and time modeling in a unique framework .

By exploring the sequence nature of video , You can create a monitoring signal for learning time information , So as to realize self supervised learning . Some recent methods follow this research route , An excuse task for self-monitoring time prediction is created ( pretext task). In this work ,shuffle. This enables the model to clearly quantify the degree of time difference in editing distance , However, the existing self-monitoring methods are usually limited to estimating the approximate difference between two videos in the time domain . for example , Previous methods often created an excuse task to predict whether the speed or playback speed of two video sequences were the same , But it ignores the details of this time difference .

Although most self supervised contrastive learning methods use a variety of data enhancements to generate positive alignments , These data enhancements provide different views of the instance , But the author developed a new method , Be able to get stronger representation from different structures through comparative learning .3D CNN The series has achieved remarkable performance in various video tasks , Include C3D、R3D、R(2+1)D etc. . because CNN Inherent characteristics of , They can capture local correlations in the time domain . however CNN The effective receptive field may limit its ability to model long-term dependence .

On the other hand ,transformer The architecture can naturally capture such long-distance dependencies using a self - attention mechanism , Each of them token Can learn to pay attention to the whole sequence , Thus meaningful context information is encoded into the video representation . Besides , When training on large enough data ,CNN The inductive bias of may limit its performance , Due to the dynamic weighting of self attention , This restriction may not apply to Transformer Occur in the .

The author thinks that , Modeling local and global dependencies is crucial for video understanding ;CNN Inductive bias and Transformer The capacity of can compensate each other . In this work , The author proposes a new cross architecture contrastive learning for self supervised video representation learning (CACL) frame .CACL Can from 3D CNN And video Transformer Generate a variety of more meaningful comparisons to learn from . The author proved that the video Transformer Can be greatly enhanced by 3D CNN Generated video representation . It produces rich high-level contextual features , And encourage 3D CNN Capture more details . This allows the two structures to work together , This is the key to improving performance .

The main contributions of this paper are summarized as follows :

  1. The author designed a new cross architecture comparative learning (CACL) frame , For self supervised video representation learning .CACL Use 3DCNN and Transformer Collaborative generation of diverse but meaningful alignments , So as to achieve more effective comparative representation learning .

  2. By explicitly measuring video and its time self-shuffle Edit distance between , A new self supervised time learning method is introduced . This helps to learn a wealth of time information , To supplement from CACL The learned expression .

  3. The author verifies the method in this paper on two downstream video tasks : Video recognition and Motion Retrieval . stay UCF101 and HMDB51 The result of the experiment shows that , The proposed CACL Can be significantly better than existing methods , Such as VideoMoCo and MoCo+BE.



      03      

Method

The author deals with video representation learning in a self supervised way . In this section , Firstly, the general framework of the proposed method is introduced . Then the proposed contrastive learning method is described in detail , And self supervised time learning based on frame level disorder prediction .

3.1. Overall Framework

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The above table shows the overall framework of this approach , The framework of this paper consists of two paths , Including a transformer Video encoder and a 3D CNN Video encoder . The self supervised learning signal is calculated by two tasks : Segment level contrast learning and frame level time prediction .

3D CNN video encoder

In this work , use 3D CNN As the main video encoder , It's also used for reasoning . whatever 3D CNN Architecture can be applied to the framework of this article . Combine the original clip with shuffle The output characteristics of the fragment concat get up , Then enter the comparison header and classification header . Both heads are fully connected feedforward networks .

Transformer video encoder

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transformer Encoder by 2D CNN and transformer Architecture Composition , As shown in the figure above . First , adopt 2D CNN Calculate each image frame of the video clip , The CNN Perform feature extraction to obtain frame level token Sequence . then , Output CNN The feature is projected to through the full connection layer 768-D Frame of token. Then the frames are sorted in chronological order token concat get up , And at frame token Add learnable embeddedness to the sequence .

Last , One 6 layer 6 head Transformer The model takes segment level feature sequence as input , The embedded output can be learned as a video representation . It is worth noting that , The feature extraction network is achieved by using a self supervised method MoCo, Use UCF101 Training set of video frames for pre training ResNet50, Its weight is frozen during the self supervised video presentation learning .

3.2. Cross-Architecture Contrastive learning

The goal of self supervised contrastive learning in this paper is to maximize the similarity between video clips with the same context , At the same time, minimize the similarity between clips from different videos . It is different from the previous contrastive learning methods , In this paper, the CACL Better joint capture of local and remote dependencies using cross architecture contrast learning signals .

Construction of positive pairs

The fundamental problem of contrastive learning lies in the design of positive and negative samples . Previous work on self supervised contrastive learning usually used various data enhancements to generate different versions of specific instances , So as to form a positive . In this work , The author enriches the antithesis from two perspectives : Embedded layer ( Use different network structures ) And the data layer .

From the perspective of the Internet ,CACL Take advantage of 3D CNN and Transformer The advantages of . Given an input video clip , Each video segment generates a video representation , Compared with the previous method , This will double the number of positive samples . In the data layer , The author of the original fragment x Random in time dimension shuffle, And get a shuffle Video clip . These two examples then cancat together .

Pictured 1 Shown , By using different data enhancement and encoder , Maximizes the similarity of the four positive pairs generated from each video clip . Express different data enhancements as ,Transformer The encoder is represented as , The three dimensional CNN The encoder is represented as , Four feature representations can then be generated for the video clip .

Negative pairs

Clips from different videos are considered negative samples . Author use MoCo The proposed momentum encoder and memory dictionary queue It further enhances comparative learning , It provides more meaningful negative samples to improve the performance of contrastive learning .

Data augmentations

The author performs data enhancement in the spatial and temporal domains of the input video clip . Be careful , Spatial enhancement is performed consistently on all frames within the clip . therefore , The author maximizes three kinds of similarity :(1) The similarity between segments calculated by the same network but performing different data extensions ;(2) The similarity between fragments with the same data expansion but calculated by different networks ;(3) Using different networks and different data to enhance the similarity between fragments .

Loss function

Formally , The author considers a case by N Random sampling of different video instances batch, Then extract a segment from each video . This will result in a batch In all N A fragment (C). The author randomly shuffle The order of each segment , Create a new set of N A fragment (). Then put each fragment and its shuffle edition concat get up , And use data enhancement for further processing .

This will generate two with different data enhancements concat Video clip . The generated clips are processed by different video coders : be based on 3D-CNN Video encoder based on Transformer The encoder . therefore , The author generates four segment level video representations for each video instance :, It is used to construct a positive alignment during comparative learning . The author makes use of InfoNCE The case discrimination idea of contrast loss :

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Where is the similarity measure between two vectors . And are two kinds of characteristics .τ It's an adjustable parameter . In this work , The author extends the contrastive learning of video representation learning to :

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among , Is from a queue of size m Of memory dictionary queue The negative sample of . As shown in the above formula , In this paper, the CACL Be able to generate more alignments than standard contrastive learning .

3.3. Temporal Prediction with Edit Distance

The goal of this article is to learn time - sensitive video representation . So , The author tries to predict video clips and their shuffle Time differences between versions to train the network . The authors believe that this time prediction task requires motion and appearance cues . This enables the model to learn meaningful time details , Thus, it is beneficial to downstream tasks . In this work , The author proposes to use the minimum editing distance (MED) To measure video clips versus shuffle The degree of time difference between versions .

MED Provides a way to measure two strings by calculating the minimum operand required to convert one string to another ( For example, words ) The difference between the methods . Mathematically speaking , Two strings a,b Between Levenshtein Distance is represented by , among :

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among , It's an indicator function , Then equal to 0, Otherwise 1. In this work , take shuffle The prediction task is described as a classification problem , The cross entropy loss is used to calculate the three-dimensional CNN Model training . Given a video clip and its shuffle edition , You can calculate :

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among m It's all shuffle Number of videos .

Uniform shuffle-degree sampling

Given a 16 Video clips of frames , The author carried out a random shuffle, And calculate the original clip and shuffle Between fragments MED. The author finds that in this example ,MED It is a slave. 0 To 16 Of discrete integers (1 With the exception of ), This allows the author to put MED The regression problem of prediction is reformulated as a classification task . However , The distribution of these discrete integers is not uniform , This may lead to classification imbalance , Make the training process unstable . Technically speaking , The author first sampled a random sample from the uniform distribution MED Count , Then a random shuffle Video clip , Until it meets the requirements of sampling MED Count . This operation makes the model well balance the label distribution in classification , This is very important for time modeling and joint learning .

Compared with other shuffle&learn method

Compared with earlier methods , Such as Shuffle&Learn、OPN and VCOP. The method in this paper focuses on degree perception , Not sequential prediction / verification , This naturally leads to the following characteristics . It can learn more meaningful time information by increasing the number of frames , The previous method is usually limited to a very small number of frames . Because with the frame / An increase in the number of fragments , The number of sequences will increase rapidly . This method can capture more detailed and meaningful differences between video clips , This enables the model to learn more abundant temporal characteristics .


      04      

experiment

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The author has studied shuffle degree prediction(SDP) The ability to learn time information from video , And compare it with the recently developed VCOP and PRP Made a comparison . The above table compares the results , Among them, the SDP Significantly better than VCOP, And achieved with PRP Quite a result .

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As shown in the table above , Express 3D CNN Right opposite of , With different data enhancements , This is equivalent to using SDP Execute the original... On the video MoCo. Use all possible alignments

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It's in this article CACL Full implementation . By adding more facing groups , Can gradually improve performance . This shows that the video of this article Transformer The encoder can provide more meaningful contrast information .

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In order to further study the influence of different positive samples on self supervised contrastive learning , The author calculated UCF101 test split 1 Average similarity of positive sample pairs in score , As shown in the table above .

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In the above table , The author shows the comparison of retrieval results between this method and different self supervised learning methods in video retrieval task , It can be seen that this method has obvious advantages .

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In the above table , The author shows the comparison of the retrieval results of this method and different self supervised learning methods in action recognition task .


      05      

summary

In this paper , A new self supervised video representation learning framework is proposed CACL. By introducing Transformer Video encoder , Designed a framework of comparative learning , It's three-dimensional CNN The comparative learning of provides a wealth of comparative samples . The author also introduces a new pretext Task to train a predictive video shuffle A model of degree . In order to verify the effectiveness of this method , The author has conducted extensive experiments on two different downstream tasks across three network architectures . Experimental results show that , In this paper, the shuffle degree prediction and transformer Video coders can encourage models to learn portable video representations , Compared with the method based on comparative learning , The features learned are heterogeneous .

Reference material

[1]https://arxiv.org/abs/2205.13313
[2]https://github.com/guoshengcv/CACL

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