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MSR-VTT: A Large Video Description Dataset for Bridging Video and Language

CVPR 2016 · Video › Video-language understanding · approach: Video-language pretraining · 1,822 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add C3D temporal features to VGG-19 frame features (ablation)SA-LSTM · MSR-VTT-10KBLEU@4: 37.6 -> 40.5Helped
add C3D temporal features to VGG-19 frame features (ablation)SA-LSTM · MSR-VTT-10KMETEOR: 28.9 -> 29.9Helped
use soft attention instead of mean pooling (ablation)LSTM with C3D+VGG-19 features · MSR-VTT-10KBLEU@4: 40.1 -> 40.5Helped
use soft attention instead of mean pooling (ablation)LSTM with C3D+VGG-19 features · MSR-VTT-10KMETEOR: 29.5 -> 29.9Helped
replace VGG-19 with VGG-16 frame features (ablation)SA-LSTM with C3D features · MSR-VTT-10KBLEU@4: 40.5 -> 40.3Hurt
replace VGG-19 with VGG-16 frame features (ablation)SA-LSTM with C3D features · MSR-VTT-10KMETEOR: 29.9 -> 29.7Hurt
increase LSTM hidden size from 128 to 512 (ablation)MP-LSTM with C3D features · MSR-VTT-10KBLEU@4: 32.5 -> 39.9Helped
increase LSTM hidden size from 128 to 512 (ablation)MP-LSTM with C3D features · MSR-VTT-10KMETEOR: 26.6 -> 29.3Helped
use temporal C3D features instead of VGG-19 frame features (ablation)MP-LSTM · MSR-VTT-10KBLEU@4: 37.3 -> 39.9Helped
use temporal C3D features instead of VGG-19 frame features (ablation)MP-LSTM · MSR-VTT-10KMETEOR: 28.7 -> 29.3Helped
use soft attention rather than mean pooling (ablation)LSTM with VGG-19 features, documentary category · MSR-VTT-10K documentaryMETEOR: 34.7 -> 28.2Hurt
use soft attention rather than mean pooling (ablation)LSTM with C3D+VGG-19 features, news category · MSR-VTT-10K newsMETEOR: 31.2 -> 36.2Helped

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