Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval

TL;DR

SearchDet is a training-free framework for long-tail object detection that leverages web-image retrieval to improve open-vocabulary detection without additional training.

Contribution

It introduces a novel, training-free method that uses web-retrieved exemplars for object detection, significantly boosting performance on long-tail datasets.

Findings

Over 48.7% mAP improvement on ODinW

Over 59.1% mAP improvement on LVIS

Stable performance despite variations in exemplars

Abstract

In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, embeds these images, and computes an input image-weighted query which is used to detect the desired concept in the image. Our proposed method is simple and training-free, yet achieves over 48.7% mAP improvement on ODinW and 59.1% mAP improvement on LVIS compared to state-of-the-art models such as GroundingDINO. We further show that our approach of basing object detection on a set of Web-retrieved exemplars is stable with respect to variations in the exemplars, suggesting a path towards eliminating costly data annotation and training procedures.