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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -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).

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8 changes: 8 additions & 0 deletions scenic/projects/README.md
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Expand Up @@ -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).
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