Add VideoMAE (#17821)
* First draft * Add VideoMAEForVideoClassification * Improve conversion script * Add VideoMAEForPreTraining * Add VideoMAEFeatureExtractor * Improve VideoMAEFeatureExtractor * Improve docs * Add first draft of model tests * Improve VideoMAEForPreTraining * Fix base_model_prefix * Make model take pixel_values of shape (B, T, C, H, W) * Add loss computation of VideoMAEForPreTraining * Improve tests * Improve model testsé * Make all tests pass * Add VideoMAE to main README * Add tests for VideoMAEFeatureExtractor * Add integration test * Improve conversion script * Rename patch embedding class * Remove VideoMAELayer from init * Update design of patch embeddings * Improve comments * Improve conversion script * Improve conversion script * Add conversion of pretrained model * Add loss verification of pretrained model * Add loss verification of unnormalized targets * Add integration test for pretraining model * Apply suggestions from code review * Fix bug to make feature extractor resize only shorter edge * Address more comments * Improve normalization of videos * Add doc examples * Move constants to dedicated script * Remove scripts * Transfer checkpoints, fix docs * Update script * Update image mean and std * Fix doc tests * Set return_tensors to NumPy by default * Revert the previous change Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
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NielsRogge
@@ -381,6 +381,8 @@
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title: Swin Transformer V2
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- local: model_doc/van
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title: VAN
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- local: model_doc/videomae
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title: VideoMAE
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- local: model_doc/vit
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title: Vision Transformer (ViT)
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- local: model_doc/vit_mae
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@@ -166,6 +166,7 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
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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.
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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.
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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.
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1. **[VideoMAE](model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
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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.
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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.
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1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
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@@ -299,6 +300,7 @@ Flax), PyTorch, and/or TensorFlow.
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| UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌ |
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| UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌ |
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| VAN | ❌ | ❌ | ✅ | ❌ | ❌ |
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| VideoMAE | ❌ | ❌ | ✅ | ❌ | ❌ |
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| ViLT | ❌ | ❌ | ✅ | ❌ | ❌ |
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| Vision Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
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| VisionTextDualEncoder | ❌ | ❌ | ✅ | ❌ | ✅ |
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@@ -118,6 +118,10 @@ Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
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[[autodoc]] AutoModelForImageClassification
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## AutoModelForVideoClassification
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[[autodoc]] AutoModelForVideoClassification
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## AutoModelForVision2Seq
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[[autodoc]] AutoModelForVision2Seq
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60
docs/source/en/model_doc/videomae.mdx
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docs/source/en/model_doc/videomae.mdx
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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# VideoMAE
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## Overview
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The VideoMAE model was proposed in [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
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VideoMAE extends masked auto encoders ([MAE](vit_mae)) to video, claiming state-of-the-art performance on several video classification benchmarks.
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The abstract from the paper is the following:
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*Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets. In this paper, we show that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP). We are inspired by the recent ImageMAE and propose customized video tube masking and reconstruction. These simple designs turn out to be effective for overcoming information leakage caused by the temporal correlation during video reconstruction. We obtain three important findings on SSVP: (1) An extremely high proportion of masking ratio (i.e., 90% to 95%) still yields favorable performance of VideoMAE. The temporally redundant video content enables higher masking ratio than that of images. (2) VideoMAE achieves impressive results on very small datasets (i.e., around 3k-4k videos) without using any extra data. This is partially ascribed to the challenging task of video reconstruction to enforce high-level structure learning. (3) VideoMAE shows that data quality is more important than data quantity for SSVP. Domain shift between pre-training and target datasets are important issues in SSVP. Notably, our VideoMAE with the vanilla ViT backbone can achieve 83.9% on Kinects-400, 75.3% on Something-Something V2, 90.8% on UCF101, and 61.1% on HMDB51 without using any extra data.*
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Tips:
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- One can use [`VideoMAEFeatureExtractor`] to prepare videos for the model. It will resize + normalize all frames of a video for you.
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- [`VideoMAEForPreTraining`] includes the decoder on top for self-supervised pre-training.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/videomae_architecture.jpeg"
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alt="drawing" width="600"/>
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<small> VideoMAE pre-training. Taken from the <a href="https://arxiv.org/abs/2203.12602">original paper</a>. </small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr).
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The original code can be found [here](https://github.com/MCG-NJU/VideoMAE).
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## VideoMAEConfig
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[[autodoc]] VideoMAEConfig
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## VideoMAEFeatureExtractor
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[[autodoc]] VideoMAEFeatureExtractor
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- __call__
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## VideoMAEModel
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[[autodoc]] VideoMAEModel
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- forward
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## VideoMAEForPreTraining
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[[autodoc]] transformers.VideoMAEForPreTraining
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- forward
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## VideoMAEForVideoClassification
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[[autodoc]] transformers.VideoMAEForVideoClassification
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- forward
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