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Stacked Cross Attention for Image-Text Matching
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“Adding images to such measures (e.g., Vilbertscore [72], TIGEr [65], SCAN [75]) or using reference-free measures (e.g., CLIPScore [59]) can help, but degraded images can be a confound in their scores.”
From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [75]
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Self-citationImproving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Bhalodia, R., Hatamizadeh, A., Tam, L., Xu, Z., Wang, X., Turkbey, E. B., & Xu, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87196-3_53 · Cited by 24
Towards Fast and Accurate Image-Text Retrieval With Self-Supervised Fine-Grained Alignment
Zhuang, J., Yu, J., Ding, Y., Qu, X., & Hu, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3280734 · Cited by 25
Ott, F., Rügamer, D., Heublein, L., Bischl, B., & Mutschler, C. (2023) · IEEE Access · doi:10.1109/access.2023.3310819 · Cited by 24
A Fusion Encoder with Multi-Task Guidance for Cross-Modal Text–Image Retrieval in Remote Sensing
Zhang, X., Li, W., Wang, X., Wang, L., Zheng, F., Wang, L., & Zhang, H. (2023) · Remote Sensing · doi:10.3390/rs15184637 · Cited by 28
MatchXML: An Efficient Text-Label Matching Framework for Extreme Multi-Label Text Classification
Ye, H., Sunderraman, R., & Ji, S. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3374750 · Cited by 26
Large-Scale Tag-Based Font Retrieval With Generative Feature Learning
Chen, T., Wang, Z., Xu, N., Jin, H., & Luo, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00921 · Cited by 25
Jamil, A., Rehman, S. U., Mahmood, K., Villar, M. G., Prola, T. A., Díez, I. D. L. T., Samad, M. A., & Ashraf, I. (2024) · IEEE Access · doi:10.1109/access.2024.3365528 · Cited by 23
RetractedEnhancing image–text matching through multi-level semantic consistency alignment
Zhu, L., Han, D., Shen, X., Chen, C., & Li, K. (2025) · The Visual Computer · doi:10.1007/s00371-025-03981-y · Cited by 23
Learning to Represent Image and Text with Denotation Graph
Zhang, B., Hu, H., Jain, V., Ie, E., & Sha, F. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.60 · Cited by 23
Self-citationHybrid Fusion with Intra- and Cross-Modality Attention for Image-Recipe Retrieval
Li, J., Xu, X., Yu, W., Shen, F., Cao, Z., Zuo, K., & Shen, H. T. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462965 · Cited by 25
Modality Disentangled Discriminator for Text-to-Image Synthesis
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mmLayout: Multi-grained MultiModal Transformer for Document Understanding
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Query-Adaptive Late Fusion for Hierarchical Fine-Grained Video-Text Retrieval
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Learnable Pillar-based Re-ranking for Image-Text Retrieval
Qu, L., Liu, M., Wang, W., Zheng, Z., Nie, L., & Chua, T. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591712 · Cited by 23
Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware Recommendations
Spillo, G., Musto, C., Polignano, M., Lops, P., de Gemmis, M., & Semeraro, G. M. (2023) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3565472.3592965 · Cited by 24
Metadata
- Authors
- Lee, K.-H., Chen, X., Hua, G., Hu, H., & He, X.
- Affiliations
- Microsoft (United States) (US)
- Jingdong (China) (CN)
- Corresponding author
- Lee, Kuang-Huei
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 212-228
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2962964995
- Cited by
- 1,402
- References
- 45
- Citations by year
- 2026: 104 · 2025: 202 · 2024: 244 · 2023: 241 · 2022: 201 · 2021: 211 · 2020: 131 · 2019: 63 · 2018: 5
- FWCI
- 30.89
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- image-text matching, cross-attention, vision-language alignment, image retrieval, text retrieval, interpretability, MS COCO, Flickr30k
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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