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

1 code implementation • 8 Aug 2023 • Haoxuan Li, Yi Bin, Junrong Liao, Yang Yang, Heng Tao Shen

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 important for effectively training the model, e. g., hard negatives make the model learn efficiently and effectively.

Image-text matching Representation Learning +1