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

• URL: http://arxiv.org/abs/2409.18733v1
• Date: Thu, 26 Sep 2024 05:14:19 GMT
• Title: Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval
• Authors: Mankeerat Sidhu, Hetarth Chopra, Ansel Blume, Jeonghwan Kim, Revanth Gangi Reddy, Heng Ji,
• Abstract summary: We introduce SearchDet, a training-free long-tail object detection framework. Our proposed method is simple and training-free, yet achieves over 48.7% mAP improvement on ODinW and 59.1% mAP improvement on LVIS.
• Score: 46.944526377710346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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