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Research map / Label-efficient and robust learning / Self-supervised and contrastive

Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning

CVPR 2023 · Label-efficient and robust learning › Self-supervised and contrastive · approach: Contrastive versus non-contrastive theory · 52 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add FNS using both audio and image adjacency matrices (ablation)NCE audio-visual localization · Flickr test (VGG-SS 10k train)CIoU (%): 77.91 -> 84.33Helped
Add FNS using both audio and image adjacency matrices (ablation)NCE audio-visual localization · VGG-SS test (VGG-SS 10k train)CIoU (%): 33.93 -> 36.92Helped
Add TNE region-wise true-negative regularization (ablation)NCE audio-visual localization · Flickr test (VGG-SS 10k train)CIoU (%): 77.91 -> 81.12Helped
Add TNE region-wise true-negative regularization (ablation)NCE audio-visual localization · VGG-SS test (VGG-SS 10k train)CIoU (%): 33.93 -> 36.04Helped
Use FNAC instead of EZ-VSL + OGLAudio-visual localization · Flickr test (Flickr 144k train)CIoU (%): 83.13 -> 78.71Hurt
Use FNAC instead of EZ-VSL + OGLAudio-visual localization · VGG-SS test (Flickr 144k train)CIoU (%): 41.01 -> 33.93Hurt
Apply FNAC at batch size 64 or lessAudio-visual localization baseline · Flickr and VGG-SSlocalization performanceMixed
Increase batch size while using FNACAudio-visual localization baseline · Flickr and VGG-SSlocalization performanceHelped

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