792dd774336314c3c27a04bb260cf2cf-Supplemental.pdf

Finally,we train our model for 8hours on asingle V100GPU. We provide an illustration of our weakly supervised phrase grounding model in Figure 4b (this supplemental). Specifically,we create context-preserving negativecaptions for an image by substituting anoun in its original caption with negativenouns, that are sampled from apretrained BERT [17] model. Forexample,inthecase where only one cross-attention layer is used, adding the sentence-level contrastive loss leads to a 2.5%intheR@1accuracy. These videos contain transcribed narrations thatareeither uploaded manually byusersor aretheoutputofanautomatic speech recognition (ASR) system.