Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Overview

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps

Here is the code for ssbassline model. We also provide OCR results/features/models. The code is built on top of M4C, where more detailed information can also be found.

Citation

If you use ssbaseline in your work, please cite:

@article{zhu2020simple,
  title={Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps},
  author={Zhu, Qi and Gao, Chenyu and Wang, Peng and Wu, Qi},
  journal={arXiv preprint arXiv:2012.05153},
  year={2020}
}

Installation

First install the repo using

git clone https://github.com/ZephyrZhuQi/ssbaseline.git ~/ssbaseline
cd ~/ssbaseline
python setup.py build develop

Getting Data

We provide SBD-Trans OCR for TextVQA and ST-VQA datasets. The corresponding OCR Faster R-CNN features and Recog-CNN features are also released.

Datasets ImDBs Object Faster R-CNN Features OCR Faster R-CNN Features OCR Recog-CNN Features
TextVQA TextVQA ImDB Open Images TextVQA SBD-Trans OCRs TextVQA SBD-Trans OCRs
ST-VQA ST-VQA ImDB ST-VQA Objects ST-VQA SBD-Trans OCRs ST-VQA SBD-Trans OCRs

Pretrained Models

We release the following pretrained models for ssbaseline on TextVQA.

For the TextVQA dataset, we release: ssbaseline trained with ST-VQA as additional data (our best model) with SBD-Trans.

Datasets Config Files (under configs/vqa/) Pretrained Models Metrics Notes
TextVQA (m4c_textvqa) m4c_textvqa/m4c_with_stvqa.yml ssbaseline_with_stvqa val accuracy - 45.53%; test accuracy - 45.66% SBD-Trans OCRs; ST-VQA as additional data

Training and Evaluation

Please follow the M4C README for the training and evaluation of the M4C model on each dataset.

Owner
ZephyrZhuQi
Visual and linguistic reasoning.
ZephyrZhuQi
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