Database Paper Browser

Back to papers

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

Summary: Chameleon leverages foundation models to generate minimal, targeted multi-modal synthetic tuples to boost coverage of under-represented groups. It couples prompt-guidance strategies with quality and outlier-detection filters to preserve semantic integrity and significantly reduce downstream unfairness. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13557
Venue
VLDB
Year
2024
Pagerank
4.1945683e-05
Overall Rank
11,068 | 23.01%
DOI
10.14778/3681954.3682014

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,223 On Fair Epsilon Net and Geometric Hitting Set 2026 VLDB 4.1945683e-05
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