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PiperOrigin-RevId: 638223241

README.md

@@ -74,6 +74,7 @@ Projects that were developed in Scenic or used it for their experiments:
 * [A Generative Approach for Wikipedia-Scale Visual Entity Recognition](https://arxiv.org/abs/2403.02041)
 * [Streaming Dense Video Captioning](https://arxiv.org/abs/2404.01297)
 * [Dense Video Object Captioning from Disjoint Supervision](https://arxiv.org/abs/2306.11729)
+* [Semantica: An Adaptable Image-Conditioned Diffusion Model](https://arxiv.org/abs/2405.14857)
 
 More information can be found in [projects](https://github.com/google-research/scenic/tree/main/scenic/projects#list-of-projects-hosted-in-scenic).
 

scenic/projects/README.md

@@ -132,6 +132,14 @@
     > trained on single modalities or tasks.
     > Details can be found in the [paper](https://arxiv.org/abs/2111.12993).
 
+*   [Semantica](modified_simple_diffusion)
+
+    > Semantica is a image-conditioned diffusion model that generates
+    > images based on the semantics of a conditioning image. It is trained
+    > exclusively on web-scale image pairs employing pretrained image encoders
+    > and semantic data-filtering.
+    > Details can be found in the [paper](https://arxiv.org/abs/2405.14857).
+
 *   [T5](t5)
 
     > Wrappers of T5 models in [t5x](https://github.com/google-research/t5x).