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Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization

NeurIPS 2018 · Video › Action recognition · approach: Contrastive video representation learning · 331 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Introduce hard negatives after an initial easy-negative training stage (ablation)AVTS · Kinetics test set (easy negatives)AVTS accuracy: 69.0 -> 78.4Helped
Use 75% easy and 25% hard negatives from the start (ablation)AVTS · Kinetics test set (easy negatives)AVTS accuracy: 69.0 -> 58.9Hurt
Use hard negatives alone from the start (ablation)AVTS · Kinetics test set (easy negatives)AVTS accuracy: 69.0 -> 52.3Hurt
Add hard negatives during a second training stage after easy-negative training (ablation)AVTS downstream features and models · UCF101, HMDB51, ESC-50, DCASE2014accuracy: UCF101 77.1 -> 85.8; HMDB51 46.4 -> 56.9; ESC-50 70.6 -> 82.3; DCASE 89.2 -> 94.1Helped
Pretrain MC3 with AVTS rather than train from scratchMC3 · UCF101 and HMDB51accuracy: UCF101 69.1 -> 85.8; HMDB51 43.9 -> 56.9Helped
Increase AVTS pretraining data from Kinetics to full AudioSetMC3 · UCF101accuracy: 85.8 -> 89.0Helped
Increase AudioSet AVTS pretraining examples (ablation)AVTS audio features · ESC-50accuracy: 77.4 -> 78.9 -> 79.5 -> 80.6Helped
Replace cross-entropy optimization from scratch with contrastive loss (ablation)AVTS · AVTS trainingoptimization convergenceHelped
Fine-tune the contrastive-loss model using cross-entropy for downstream feature learning (ablation)AVTS · Action recognition and audio classificationdownstream performanceNo clear effect
Use super-hard negatives during training (ablation)AVTS · AVTSAVTS performanceHurt
Use thresholded contrastive distance instead of a cross-entropy-finetuned classifier (ablation)AVTS · Kinetics test set with mixed easy and hard negativesAVTS accuracy: 65.6% vs 70.3%Hurt
Use both audio and video streams rather than video only (ablation)AVTS action recognition model · UCF101accuracy: 85.8% -> 87.0%Helped

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