Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval
<|reference_start|>Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval: 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.<|reference_end|>
@article{sidhu2024search,
    title={Search and Detect: Training-Free Long Tail Object Detection via
  Web-Image Retrieval},
    author={Mankeerat Sidhu, Hetarth Chopra, Ansel Blume, Jeonghwan Kim, Revanth
  Gangi Reddy, Heng Ji},
    journal={arXiv preprint arXiv:2409.18733},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18733},
    primaryClass={cs.CV}
}