  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.
