Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval
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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 16.81% mAP improvement on ODinW and 59.85% 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.1
