FLAME Diffuser: Grounded Wildfire Image Synthesis using Mask Guided
  Diffusion
Hao Wang Sayed Pedram Haeri Boroujeni Xiwen Chen Ashish Bastola Huayu Li Abolfazl Razi
The rise of machine learning in recent years has brought benefits to various
research fields such as wide fire detection. Nevertheless, small object
detection and rare object detection remain a challenge. To address this
problem, we present a dataset automata that can generate ground truth paired
datasets using diffusion models. Specifically, we introduce a mask-guided
diffusion framework that can fusion the wildfire into the existing images while
the flame position and size can be precisely controlled. In advance, to fill
the gap that the dataset of wildfire images in specific scenarios is missing,
we vary the background of synthesized images by controlling both the text
prompt and input image. Furthermore, to solve the color tint problem or the
well-known domain shift issue, we apply the CLIP model to filter the generated
massive dataset to preserve quality. Thus, our proposed framework can generate
a massive dataset of that images are high-quality and ground truth-paired,
which well addresses the needs of the annotated datasets in specific tasks.