Video inpainting is the process of filling in missing or corrupted parts of a video. This technique is used in various applications including video editing, security cameras, and medical imaging. One model used for video inpainting is the FuseFormer, which utilizes a specialized block called the FuseFormer block.
What is a FuseFormer Block?
A FuseFormer block is a modified version of the standard Transformer block used in natural language processing. The Transformer block consists of two parts: a self-attention mechanism and a feed forward network. In the FuseFormer block, the feed forward network is replaced with a Fusion Feed Forward Network (F3N).
How Does F3N Work?
The F3N brings no extra parameters into the standard feed forward network. The difference is that F3N inserts a soft-split and a soft composite operation between the two layers of Multi-Layer Perceptrons (MLPs). The soft-split operation creates three separate pathways for each input representation. The output of each pathway is then combined using the soft composite operation. This process helps the model to better capture the temporal relationships between frames in videos.
What is the Purpose of the FuseFormer Block?
The purpose of the FuseFormer block is to improve the accuracy of video inpainting. The standard Transformer block is designed for language processing, which does not have the same temporal relationships as videos. By replacing the feed forward network with the F3N, the model can better represent the relationships between frames in a video, resulting in more accurate inpainting.
Overall, the FuseFormer block is a crucial part of the FuseFormer model for video inpainting. Its use of the F3N helps the model to better capture the temporal relationships between frames in videos, resulting in more accurate and realistic inpainting.
