# Bridging the Gap Between Object Detection and User Intent via Query-Modulation

Marco Fornoni, Chaochao Yan, Liangchen Luo, Kimberly Wilber, Alex Stark, Yin Cui, Boqing Gong, Andrew Howard.
arXiv preprint. 2021.

## Abstract

When interacting with objects through cameras, or pictures, users often have a specific intent. For example, they may want to perform a visual search. With most object detection models relying on image pixels as their sole input, undesired results are not uncommon. Most typically: lack of a high-confidence detection on the object of interest, or detection with a wrong class label. The issue is especially severe when operating capacity-constrained mobile object detectors on-device. In this paper we investigate techniques to modulate mobile detectors to explicitly account for the user intent, expressed as an embedding of a simple query. Compared to standard detectors, query-modulated detectors show superior performance at detecting objects for a given user query. Thanks to large-scale training data synthesized from standard object detection annotations, query-modulated detectors also outperform a specialized referring expression recognition system. Query-modulated detectors can also be trained to simultaneously solve for both localizing a user query and standard detection, even outperforming standard mobile detectors at the canonical COCO task.

## BibTex

@article{Fornoni21QMD,
author = {Fornoni, Marco and Yan, Chaochao and Luo, Liangchen and Wilber, Kimberly and Stark, Alex and Cui, Yin and Gong, Boqing and Howard, Andrew},
title = {Bridging the Gap Between Object Detection and User Intent via Query-Modulation},
journal = {arXiv preprint arXiv:2106.10258},
year = {2021}
}