Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points
<|reference_start|>Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points: In recent years, research on point weakly supervised object detection (PWSOD) methods in the field of computer vision has attracted people's attention. However, existing pseudo labels generation methods perform poorly in a small amount of supervised annotation data and dense object detection tasks. We consider the generation of weakly supervised pseudo labels as the result of model's sparse output, and propose a method called Sparse Generation to make pseudo labels sparse. It constructs dense tensors through the relationship between data and detector model, optimizes three of its parameters, and obtains a sparse tensor via coordinated calculation, thereby indirectly obtaining higher quality pseudo labels, and solving the model's density problem in the situation of only a small amount of supervised annotation data can be used. On two broadly used open-source datasets (RSOD, SIMD) and a self-built dataset (Bullet-Hole), the experimental results showed that the proposed method has a significant advantage in terms of overall performance metrics, comparing to that state-of-the-art method.<|reference_end|>
@article{ma2024sparse,
    title={Sparse Generation: Making Pseudo Labels Sparse for weakly supervision
  with points},
    author={Tian Ma, Chuyang Shang, Wanzhu Ren, Yuancheng Li, Jiiayi Yang, Jiali
  Qian},
    journal={arXiv preprint arXiv:2403.19306},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19306},
    primaryClass={cs.CV}
}