Diffusion-based Data Augmentation for Object Counting Problems

Understand

Crowd counting is an important problem in computer vision due to its wide range of applications in image understanding.

• Currently, this problem is typically addressed using deep learning approaches, such as Convolutional Neural Networks (CNNs) and Transformers.
• However, deep networks are data-driven and are prone to overfitting, especially when the available labeled crowd dataset is limited.
• To overcome this limitation, we have designed a pipeline that utilizes a diffusion model to generate extensive training data.