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xmodaler

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).

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Created 2021-06-25 · Updated 2026-10-13 · #12141 today
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README

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 BLEU@1 BLEU@2 BLEU@3 BLEU@4 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 BLEU@1 BLEU@2 BLEU@3 BLEU@4 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 BLEU@1 BLEU@2 BLEU@3 BLEU@4 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 BLEU@1 BLEU@2 BLEU@3 BLEU@4 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}
}
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