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Visual Attention Network (VAN) (#16027)

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* encoder works

* addded files

* norm in stage

* convertion script

* tests

* fix copies

* make fix-copies

* fixed __init__

* make fix-copies

* fix

* shapiro test needed

* make fix-copie

* minor changes

* make style + quality

* minor refactor conversion script

* rebase + tests

* removed unused variables

* updated doc

* toctree

* CI

* doc

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* resolved conversations

* make fixup

* config passed to modules

* config passed to modules

* Apply suggestions from code review

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* conversations

* conversations

* copyrights

* normal test

* tests

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
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Francesco Saverio Zuppichini
2022-03-15 08:47:12 +01:00
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@@ -142,6 +142,7 @@ conversion utilities for the following models.
1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
1. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
1. **[ViLT](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
1. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
@@ -250,6 +251,7 @@ Flax), PyTorch, and/or TensorFlow.
| TrOCR | ❌ | ❌ | ✅ | ❌ | ❌ |
| UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌ |
| UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌ |
| VAN | ❌ | ❌ | ✅ | ❌ | ❌ |
| ViLT | ❌ | ❌ | ✅ | ❌ | ❌ |
| Vision Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
| VisionTextDualEncoder | ❌ | ❌ | ✅ | ❌ | ✅ |