Blended Diffusion enables a zero-shot local text-guided image editing of natural images.
Given an input image $x$, an input mask $m$ and a target guiding text $t$ - the method enables to change the masked area within the image corresponding the the guiding text s.t. the unmasked area is left unchanged.

Syntax Heat Parse Tree are heatmaps over parse trees, similar to ["heat trees"](https://doi.org/10.1371/journal.pcbi.1005404) in biology.

Syntax Heat Parse Tree

KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations

Blended Diffusion

Blended Diffusion for Text-driven Editing of Natural Images

A **Spatially Separable Convolution** decomposes a [convolution](https://paperswithcode.com/method/convolution) into two separate operations. In regular convolution, if we have a 3 x 3 kernel then we directly convolve this with the image. We can divide a 3 x 3 kernel into a 3 x 1 kernel and a 1 x 3 kernel. Then, in spatially separable convolution, we first convolve the 3 x 1 kernel then the 1 x 3 kernel. This requires 6 instead of 9 parameters compared to regular convolution, and so it is more parameter efficient (additionally less matrix multiplications are required).

Image Source: [Kunlun Bai](https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215)

Source	Blended Diffusion for Text-driven Editing of Natural Images
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com