• X-modaler
• • Installation
  • • Requiremenets
  • Getting Started
  • • Training & Evaluation in Command Line
  • Model Zoo and Baselines
  • • • Image Captioning on MSCOCO (Cross-Entropy Loss)
      • Image Captioning on MSCOCO (CIDEr Score Optimization)
      • Video Captioning on MSVD
      • Video Captioning on MSR-VTT
      • Visual Question Answering
      • Caption-based image retrieval on Flickr30k
      • Visual commonsense reasoning
  • License
  • Citing X-modaler

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}
}