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Reconstruction Network for Video Captioning

CVPR 2018 · Video › Video-language understanding · approach: Encoder-decoder video captioning · 340 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add global video-feature reconstruction to SA-LSTMSA-LSTM (Inception-V4) · MSR-VTT-10K testBLEU-4: 36.3 -> 38.3Helped
Add local attention-based video-feature reconstruction to SA-LSTMSA-LSTM (Inception-V4) · MSR-VTT-10K testBLEU-4: 36.3 -> 39.1Helped
Replace global reconstruction with local attention-based reconstruction (ablation)RecNet · MSR-VTT-10K testBLEU-4: 38.3 -> 39.1Helped
Use temporal attention rather than mean pooling to aggregate frame featuresSA-LSTM versus MP-LSTM with VGG19 · MSR-VTT-10K testBLEU-4: 34.8 -> 35.6Helped
Use temporal attention rather than mean pooling to aggregate frame featuresSA-LSTM versus MP-LSTM with VGG19 · MSR-VTT-10K testMETEOR: 24.7 -> 25.4Helped
Add global reconstruction to S2VTS2VT (Inception-V4) · MSVD testBLEU-4: 39.6 -> 42.9Helped
Add local reconstruction to S2VTS2VT (Inception-V4) · MSVD testBLEU-4: 39.6 -> 43.7Helped
Add global video-feature reconstruction to SA-LSTMSA-LSTM (Inception-V4) · MSVD testBLEU-4: 45.3 -> 51.1Helped
Add local video-feature reconstruction to SA-LSTMSA-LSTM (Inception-V4) · MSVD testBLEU-4: 45.3 -> 52.3Helped
Set reconstruction-loss weight above zero (ablation)RecNet · MSR-VTT-10KBLEU-4Helped
Set reconstruction-loss weight too large (ablation)RecNet · MSR-VTT-10KBLEU-4Hurt

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