SeeSaw: Interactive Ad-hoc Search Over Image Databases
Author(s)
Moll, Oscar
Favela, Manuel
Madden, Samuel
Gadepally, Vijay
Cafarella, Michael
Date Issued
December 12, 2023
Journal
Proceedings of the ACM on Management of Data
Publisher
ACM
Citation
Moll, Oscar, Favela, Manuel, Madden, Samuel, Gadepally, Vijay and Cafarella, Michael. 2023. "SeeSaw: Interactive Ad-hoc Search Over Image Databases." Proceedings of the ACM on Management of Data, 1 (4 (SIGMOD)).
Version
Final published version
Abstract
As image datasets become ubiquitous, the problem of ad-hoc searches over image data is increasingly important. Many high level data tasks in machine learning, such as constructing datasets for training and testing object detectors, imply finding ad-hoc objects or scenes within large image datasets as a key sub-problem. New foundational visual-semantic embeddings trained on massive web datasets such as CLIP can help users start searches on their own data, but we find there is a long tail of queries where these models fall short in practice. SeeSaw is a system for interactive ad-hoc searches on image datasets that integrates state-of-the-art embeddings like CLIP with user feedback in the form of box annotations to help users quickly locate images of interest in their data even in the long-tail of harder queries. One key challenge for SeeSaw is that many sensible approaches to incorporating feedback into future results, including state of the art active-learning algorithms, can worsen results compared to introducing no feedback at all, partly due to CLIP’s high average performance. Therefore, SeeSaw employs several algorithms to transform user feedback into consistent improvements over CLIP alone. We compare SeeSaw’s accuracy to both using CLIP alone as well as to a state-of-the-art active-learning baseline and find SeeSaw consistently helps improve results for users across four datasets and more than a thousand queries. SeeSaw increases Average Precision (AP) on search tasks by an average of .08 on a wide benchmark (from a base of .72), and by a .27 on a subset of harder queries where CLIP alone performs poorly.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Lincoln Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3626754
