Abstract
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.
Abstract (translated)
近年来，计算机视觉领域关于点弱监督物体检测（PWSOD）方法的研究引起了人们的关注。然而，现有的伪标签生成方法在少量监督标注数据和密集物体检测任务上表现不佳。我们考虑弱监督伪标签生成是模型稀疏输出的结果，并提出了名为Sparse Generation的方法来使伪标签稀疏。它通过数据与检测器模型之间的关系构建密集向量，优化三个参数，并通过协同计算获得稀疏向量，从而间接地获得更高质量的伪标签，解决了仅少量监督标注数据情况下模型密度的难题。在两个广泛使用的开源数据集（RSOD，SIMD）和自建数据集（Bullet-Hole）上进行的实验结果表明，与最先进的 methods相比，所提出的方法在整体性能指标上具有显著的优势。
URL
https://arxiv.org/abs/2403.19306
