Few-Shot Object Detection with Sparse Context Transformers
<|reference_start|>Few-Shot Object Detection with Sparse Context Transformers: Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluate the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art.<|reference_end|>
@article{mei2024few-shot,
    title={Few-Shot Object Detection with Sparse Context Transformers},
    author={Jie Mei, Mingyuan Jiu, Hichem Sahbi, Xiaoheng Jiang, Mingliang Xu},
    journal={arXiv preprint arXiv:2402.09315},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09315},
    primaryClass={cs.CV}
}