Abstract
We review the current schemes of text-image matching models and propose improvements for both training and inference. First, we empirically show limitations of two popular loss (sum and max-margin loss) widely used in training text-image embeddings and propose a trade-off: a kNN-margin loss which 1) utilizes information from hard negatives and 2) is robust to noise as all K-most hardest samples are taken into account, tolerating pseudo negatives and outliers. Second, we advocate the use of Inverted Softmax (IS) and Cross-modal Local Scaling (CSLS) during inference to mitigate the so-called hubness problem in high-dimensional embedding space, enhancing scores of all metrics by a large margin.- Anthology ID:
- P19-2023
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 169–176
- Language:
- URL:
- https://aclanthology.org/P19-2023
- DOI:
- 10.18653/v1/P19-2023
- Cite (ACL):
- Fangyu Liu and Rongtian Ye. 2019. A Strong and Robust Baseline for Text-Image Matching. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 169–176, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- A Strong and Robust Baseline for Text-Image Matching (Liu & Ye, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-2023.pdf
- Data
- COCO, Flickr30k