DBScholar

Back to papers

Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities

Summary: Chameleon uses foundation models for fairness-aware, multimodal data augmentation, generating few tuples to improve minority coverage. Quality/outlier checks and guidance strategies preserve semantic integrity while reducing downstream model unfairness. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13745
Venue
VLDB
Year
2024
Pagerank
5.2755515e-05
Overall Rank
9,388 | 35.60%
DOI
10.14778/3681954.3682014

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{erfanian_vldb24,
        title = {{Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities}},
        author = {Erfanian, Mahdi and Jagadish, H. V. and Asudeh, Abolfazl},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {3470--3483},
        doi = {10.14778/3681954.3682014},
        url = {https://doi.org/10.14778/3681954.3682014},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers