# Unconstrained Foreground Object Search

## TL;DR

This paper introduces a novel unconstrained foreground object search method that encodes background images and foreground objects in a shared latent space, enabling efficient retrieval across diverse categories.

## Contribution

It presents a scalable, cost-free approach to create large training datasets and a new UFO search framework that surpasses existing constrained methods.

## Key findings

- Outperforms baseline methods in quantitative experiments.
- Human perception tests favor the UFO search results.
- Supports diverse semantic categories in object retrieval.

## Abstract

Many people search for foreground objects to use when editing images. While existing methods can retrieve candidates to aid in this, they are constrained to returning objects that belong to a pre-specified semantic class. We instead propose a novel problem of unconstrained foreground object (UFO) search and introduce a solution that supports efficient search by encoding the background image in the same latent space as the candidate foreground objects. A key contribution of our work is a cost-free, scalable approach for creating a large-scale training dataset with a variety of foreground objects of differing semantic categories per image location. Quantitative and human-perception experiments with two diverse datasets demonstrate the advantage of our UFO search solution over related baselines.