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SeeSaw: Interactive Ad-hoc Search Over Image Databases

Summary: SeeSaw blends CLIP-like embeddings with box feedback for ad-hoc search in large image collections. Robust methods mitigate feedback-driven degradation, yielding avg AP gains +0.08 from 0.72 and +0.27 on hard queries across four datasets and 1k+ queries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6823
Venue
SIGMOD
Year
2023
Pagerank
5.6599652e-05
Overall Rank
7,266 | 50.15%
DOI
10.1145/3626754

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{moll_sigmod23,
        title = {{SeeSaw: Interactive Ad-hoc Search Over Image Databases}},
        author = {Moll, Oscar and Favela, Manuel and Madden, Samuel and Gadepally, Vijay and Cafarella, Michael},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626754},
        url = {https://dl.acm.org/doi/10.1145/3626754},
        year = {2023}
}

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