← all papers · overview

Seesaw: Interactive Ad-hoc Search Over Image Databases

2022 · Oscar Moll, Manuel Favela, Samuel Madden, et al.

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 Contrastive Language-Image Pre-Training (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, in practice, many sensible approaches to incorporating feedback into future results, including sta

Authors

(none)

Tags

  • ANN Search

Stats

  • citations4
  • S2 citations—
  • github stars0
  • HF likes0
  • heat score5.24
  • arxiv keymoll2022seesaw

Related papers