most citedYour Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination

41 citations · 41 across the 1 of their papers we have counts for

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cs.CV2023★ 41 cited

Your Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination

Haoxuan Li, Yi Bin, Junrong Liao +2

Most existing image-text matching methods adopt triplet loss as the optimization objective, and choosing a proper negative sample for the triplet of <anchor, positive, negative> is…