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https://github.com/fundamentalvision/Uni-Perceiver


https://github.com/fundamentalvision/Uni-Perceiver

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README 

        
# Uni-Perceiver



This repository contains training (pre-training, fine-tuning, prompt-tuning), evaluation code and pretrained models for the following papers:



> [Uni-Perceiver](https://arxiv.org/abs/2112.01522): Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks, CVPR 2022.



> [Uni-Perceiver-MoE](https://arxiv.org/abs/2206.04674): Learning Sparse Generalist Models with Conditional MoEs, NeurIPS 2022.



## Introduction



__Uni-Perceiver__ is a generalist model (generic perception model) that can process a variety of modalities and tasks with unified modeling and shared

parameters. Different perception tasks are modeled as the same formulation, that is, finding the maximum likelihood target for each input through the similarity of their representations. Meanwhile,  Uni-Perceiver is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage.

Thanks to the unified formulation, it shows the ability of zero-shot inference on novel tasks, and shows promising performance close to or on par with SOTA results by prompt tuning or finetuning.



![UnPerceiver-intro](./figs/overview.png)



In __Uni-Perceiver-MoE__, we found that the interference among different tasks and modalities can lead to performance degradation of generalist models on some tasks compared with task-specialized models. We introduce the Conditional Mixture-of-Experts (Conditional MoEs) to mitigate such interference. By incorporating the proposed Conditional MoEs, Uni-Perceiver-MoE can effectively mitigate the interference across tasks and modalities, and achieves state-of-the-art results on a series of downstream tasks via prompt tuning on 1% of downstream data. Moreover, the introduction of Conditional MoEs still holds the generalization ability of generalist models to conduct zero-shot inference on new tasks,



![UnPerceiver-moe-intro](./figs/overview_moe.png)



## Main Results and Pretrained Models



### Base Models



  

    Task

    Image Classification

    Image Caption

    Image Retrieval

    Video ClassificationVideo CaptionVideo Retrieval

  

  

    DatasetImageNet-1kMSCOCOFlickr30kMSCOCOFlickr30kKinetics-400MSVDMSVD

  

  

    SplitILSVRC 2012 valKarpathy testtestKarpathy testtesttest-devvalvalval

  

  

    MetricAcc@1BLEU-4BLEU-4R@1 i2tR@1 t2iR@1 i2tR@1 t2iAcc@1BLEU-4R@1 v2tR@1 t2v

  

  

  

    Uni-PerceiverBASE w/o Tuning79.2 32.014.7 64.9 50.7 82.3 71.1 74.5 22.6 50.338.7 

  

  

    Uni-PerceiverBASE PT (1%)80.9 35.530.268.4 51.9 91.0 76.0 74.8 59.5 62.7 43.8 

  

   

    Uni-PerceiverBASE FT (100%)84.036.4 31.2 69.853.9 92.777.577.7 63.3 62.845.8 

  



  

    Uni-Perceiver-MoEBASE w/o Tuning80.3 33.215.9 64.6 51.6 82.1 75.8 76.8 23.4 52.840.0 

  

  

    Uni-Perceiver-MoEBASE PT (1%)82.0 36.830.768.9 52.6 91.3 78.5 77.2 60.0 65.6 45.3 

  

   

    Uni-Perceiver-MoEBASE FT (100%)84.537.3 32.4 70.554.1 93.679.879.3 65.4 65.047.8 

  



### Large Models



  

    Task

    Image Classification

    Image Caption

    Image Retrieval

    Video ClassificationVideo CaptionVideo Retrieval

  

  

    DatasetImageNet-1kMSCOCOFlickr30kMSCOCOFlickr30kKinetics-400MSVDMSVD

  

  

    SplitILSVRC 2012 valKarpathy testtestKarpathy testtesttest-devvalvalval

  

  

    MetricAcc@1BLEU-4BLEU-4R@1 i2tR@1 t2iR@1 i2tR@1 t2iAcc@1BLEU-4R@1 v2tR@1 t2v

  

  

    Uni-PerceiverLARGE w/o Tuning82.7  35.3  15.1 67.8 54.1  83.7 74.2  79.524.7  45.4 34.2 

  

  

    Uni-PerceiverLARGE PT (1%)84.2 38.6  32.9 73.3 56.2 92.1  80.0 80.0 67.2 65.5 48.6 

  

   

    Uni-PerceiverLARGE FT (100%)86.2  39.2  35.5 74.4 57.9 94.7  82.181.9 68.3  65.2 50.8 

  



  

    Uni-Perceiver-MoELARGE w/o Tuning83.4  35.5  15.8 67.9 55.3  83.6 75.9  82.124.6  45.7 41.9 

  

  

    Uni-Perceiver-MoELARGE PT (1%)84.9 39.3  33.7 73.3 57.1 92.4  80.6 83.0 67.6 66.4 50.3 

  

   

    Uni-Perceiver-MoELARGE FT (100%)86.4  40.5  36.2 74.7 58.3 94.1  83.784.2 68.9  67.6 52.3 

  



  * The numbers are slightly better than the original paper of Uni-Perceiver, which are from the reproduced version of Uni-Perceiver used as the baseline of [Uni-Perceiver-MoE](https://arxiv.org/abs/2206.04674).

  * The image resolution for all tasks is `224x224`.

  * See [OtherResults.md](data/other_results.md) for results on more tasks and datasets.



## Usage

### Requirements

* Linux, CUDA>=10.1, GCC>=5.4



* Python >=3.7



* pytorch >= 1.8.0 



* JAVA >= 1.8 (for caption task evaluation)



### Installation

```bash

git clone https://github.com/fundamentalvision/Uni-Perceiver

cd Uni-Perceiver

pip install -r requirements.txt

```



### Data

See [prepare_data.md](data/prepare_data.md).



### Pre-trained Model Weights

See [checkpoints.md](data/checkpoints.md).



### Pre-training 

See [pretraining.md](data/pretraining.md).



### Fine-tuning 

See [finetuning.md](data/finetuning.md).



###  Prompt-tuning



See [prompt_tuning.md](data/prompt_tuning.md).



###  Inference



See [inference.md](data/inference.md).



### TODO 



* release more pretrained models

  - [ ]  Uni-Perceiver Tiny model

  - [ ]  Uni-Perceiver Small model

  - [ ]  Uni-Perceiver Huge model



* support more datasets and tasks



## License

Uni-Perceiver is licensed under the [Apache-2.0 License](./LICENSE).







## Citing Uni-Perceiver

If you find Uni-Perceiver useful in your research, please consider giving a star ⭐ and citing:

```bibtex

 @article{zhu2021uni,

  title={Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks},

  author={Zhu, Xizhou and Zhu, Jinguo and Li, Hao and Wu, Xiaoshi and Wang, Xiaogang and Li, Hongsheng and Wang, Xiaohua and Dai, Jifeng},

  journal={arXiv preprint arXiv:2112.01522},

  year={2021}



}

```



```bibtex

@article{zhu2022uni,

  title={Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs},

  author={Zhu, Jinguo and Zhu, Xizhou and Wang, Wenhai and Wang, Xiaohua and Li, Hongsheng and Wang, Xiaogang and Dai, Jifeng},

  journal={arXiv preprint arXiv:2206.04674},

  year={2022}

}

```



### Acknowledgements

Many thanks to following codes that help us a lot in building this codebase:

* [Detectron2](https://github.com/facebookresearch/detectron2)

* [X-modaler](https://github.com/YehLi/xmodaler)

* [deit](https://github.com/facebookresearch/deit)

* [VL-BERT](https://github.com/jackroos/VL-BERT)

* [TimeSformer](https://github.com/facebookresearch/TimeSformer)

* [CLIP](https://github.com/openai/CLIP)