Sparse Generation: Making Pseudo Labels Sparse for weakly supervision
  with points
Tian Ma Chuyang Shang Wanzhu Ren Yuancheng Li Jiiayi Yang Jiali Qian
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.