Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points

• URL: http://arxiv.org/abs/2403.19306v1
• Date: Thu, 28 Mar 2024 10:42:49 GMT
• Title: Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points
• Authors: Tian Ma, Chuyang Shang, Wanzhu Ren, Yuancheng Li, Jiiayi Yang, Jiali Qian,
• Abstract summary: We consider the generation of weakly supervised pseudo labels as the result of model's sparse output. We propose a method called Sparse Generation to make pseudo labels sparse.
• Score: 2.2241974678268903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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