X-modaler

X-modaler is a versatile and high-performance codebase for cross-modal analytics (e.g., image captioning, video captioning, vision-language pre-training, visual question answering, visual commonsense reasoning, and cross-modal retrieval). This codebase unifies comprehensive high-quality modules in state-of-the-art vision-language techniques, which are organized in a standardized and user-friendly fashion.

The original paper can be found here.

Installation

See installation instructions.

Requiremenets

  • Linux or macOS with Python ≥ 3.6
  • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
  • fvcore
  • pytorch_transformers
  • jsonlines
  • pycocotools

Getting Started

See Getting Started with X-modaler

Training & Evaluation in Command Line

We provide a script in “train_net.py”, that is made to train all the configs provided in X-modaler. You may want to use it as a reference to write your own training script.

To train a model(e.g., UpDown) with “train_net.py”, first setup the corresponding datasets following datasets, then run:

# Teacher Force
python train_net.py --num-gpus 4 \
 	--config-file configs/image_caption/updown.yaml

# Reinforcement Learning
python train_net.py --num-gpus 4 \
 	--config-file configs/image_caption/updown_rl.yaml

Model Zoo and Baselines

A large set of baseline results and trained models are available here.

Image Captioning
Attention Show, attend and tell: Neural image caption generation with visual attention ICML 2015
LSTM-A3 Boosting image captioning with attributes ICCV 2017
Up-Down Bottom-up and top-down attention for image captioning and visual question answering CVPR 2018
GCN-LSTM Exploring visual relationship for image captioning ECCV 2018
Transformer Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning ACL 2018
Meshed-Memory Meshed-Memory Transformer for Image Captioning CVPR 2020
X-LAN X-Linear Attention Networks for Image Captioning CVPR 2020
Video Captioning
MP-LSTM Translating Videos to Natural Language Using Deep Recurrent Neural Networks NAACL HLT 2015
TA Describing Videos by Exploiting Temporal Structure ICCV 2015
Transformer Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning ACL 2018
TDConvED Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning AAAI 2019
Vision-Language Pretraining
Uniter UNITER: UNiversal Image-TExt Representation Learning ECCV 2020
TDEN Scheduled Sampling in Vision-Language Pretraining
with Decoupled Encoder-Decoder Network
AAAI 2021

Image Captioning on MSCOCO (Cross-Entropy Loss)

Name Model [email protected] [email protected] [email protected] [email protected] METEOR ROUGE-L CIDEr-D SPICE
LSTM-A3 GoogleDrive 75.3 59.0 45.4 35.0 26.7 55.6 107.7 19.7
Attention GoogleDrive 76.4 60.6 46.9 36.1 27.6 56.6 113.0 20.4
Up-Down GoogleDrive 76.3 60.3 46.6 36.0 27.6 56.6 113.1 20.7
GCN-LSTM GoogleDrive 76.8 61.1 47.6 36.9 28.2 57.2 116.3 21.2
Transformer GoogleDrive 76.4 60.3 46.5 35.8 28.2 56.7 116.6 21.3
Meshed-Memory GoogleDrive 76.3 60.2 46.4 35.6 28.1 56.5 116.0 21.2
X-LAN GoogleDrive 77.5 61.9 48.3 37.5 28.6 57.6 120.7 21.9
TDEN GoogleDrive 75.5 59.4 45.7 34.9 28.7 56.7 116.3 22.0

Image Captioning on MSCOCO (CIDEr Score Optimization)

Name Model [email protected] [email protected] [email protected] [email protected] METEOR ROUGE-L CIDEr-D SPICE
LSTM-A3 GoogleDrive 77.9 61.5 46.7 35.0 27.1 56.3 117.0 20.5
Attention GoogleDrive 79.4 63.5 48.9 37.1 27.9 57.6 123.1 21.3
Up-Down GoogleDrive 80.1 64.3 49.7 37.7 28.0 58.0 124.7 21.5
GCN-LSTM GoogleDrive 80.2 64.7 50.3 38.5 28.5 58.4 127.2 22.1
Transformer GoogleDrive 80.5 65.4 51.1 39.2 29.1 58.7 130.0 23.0
Meshed-Memory GoogleDrive 80.7 65.5 51.4 39.6 29.2 58.9 131.1 22.9
X-LAN GoogleDrive 80.4 65.2 51.0 39.2 29.4 59.0 131.0 23.2
TDEN GoogleDrive 81.3 66.3 52.0 40.1 29.6 59.8 132.6 23.4

Video Captioning on MSVD

Name Model [email protected] [email protected] [email protected] [email protected] METEOR ROUGE-L CIDEr-D SPICE
MP-LSTM GoogleDrive 77.0 65.6 56.9 48.1 32.4 68.1 73.1 4.8
TA GoogleDrive 80.4 68.9 60.1 51.0 33.5 70.0 77.2 4.9
Transformer GoogleDrive 79.0 67.6 58.5 49.4 33.3 68.7 80.3 4.9
TDConvED GoogleDrive 81.6 70.4 61.3 51.7 34.1 70.4 77.8 5.0

Video Captioning on MSR-VTT

Name Model [email protected] [email protected] [email protected] [email protected] METEOR ROUGE-L CIDEr-D SPICE
MP-LSTM GoogleDrive 73.6 60.8 49.0 38.6 26.0 58.3 41.1 5.6
TA GoogleDrive 74.3 61.8 50.3 39.9 26.4 59.4 42.9 5.8
Transformer GoogleDrive 75.4 62.3 50.0 39.2 26.5 58.7 44.0 5.9
TDConvED GoogleDrive 76.4 62.3 49.9 38.9 26.3 59.0 40.7 5.7

Visual Question Answering

Name Model Overall Yes/No Number Other
Uniter GoogleDrive 70.1 86.8 53.7 59.6
TDEN GoogleDrive 71.9 88.3 54.3 62.0

Caption-based image retrieval on Flickr30k

Name Model R1 R5 R10
Uniter GoogleDrive 61.6 87.7 92.8
TDEN GoogleDrive 62.0 86.6 92.4

Visual commonsense reasoning

Name Model Q -> A QA -> R Q -> AR
Uniter GoogleDrive 73.0 75.3 55.4
TDEN GoogleDrive 75.0 76.5 57.7

License

X-modaler is released under the Apache License, Version 2.0.

Citing X-modaler

If you use X-modaler in your research, please use the following BibTeX entry.

@inproceedings{Xmodaler2021,
  author =       {Yehao Li, Yingwei Pan, Jingwen Chen, Ting Yao, and Tao Mei},
  title =        {X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics},
  booktitle =    {Proceedings of the 29th ACM international conference on Multimedia},
  year =         {2021}
}

GitHub

View Github