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简体中文 | English

Introduction

PaddleMM mainly based on Baidu PaddlePaddle platform, compatible with PyTorch version, aiming to provide modal joint learning and cross-modal learning algorithm model libraries, providing efficient solutions for processing multi-modal data such as images and texts, which promote applications of multi-modal machine learning.

Authors of PaddleMM:

Recent Updates

2022.2.23

  • Add model BFAN

more

Features

  • Multiple task scenarios: PaddleMM provides a variety of multi-modal learning task algorithm model libraries such as multi-modal fusion, cross-modal retrieval, image caption, and supports user-defined data and training.
  • Successful industrial applications: There are related practical applications based on the PaddleMM, such as sneaker authenticity identification, image description, rumor detection, etc.

Visualization

  • Sneaker authenticity identification
For more information, please visit our website [Ysneaker](http://www.ysneaker.com/) !
  • more visualization

Enterprise Application

  • Cooperation with Baidu TIC Smart Recruitment Resume analysis, based on multi-modal fusion algorithm and successfully implemented.

Framework

PaddleMM includes the paddle version paddlemm package and the torch version torchmm, which consists of the following three modules:

  • Data processing: Provide a unified data interface and multiple data processing formats.
  • Model library: Including multi-modal fusion, cross-modal retrieval, image caption, and multi-task algorithms.
  • Trainer: Set up a unified training process and related score calculations for each task.

Use

Download the toolkit:

git clone https://github.com/njustkmg/PaddleMM.git
  • Data construction instructions here
  • Dependent files download here

Paddle Example:

from paddlemm import PaddleMM

# config: Model running parameters, see configs/
# data_root: Path to dataset
# image_root: Path to images
# gpu: Which gpu to use

runner = PaddleMM(config='configs/cmml.yml',
                  data_root='data/COCO', 
                  image_root='data/COCO/images', 
                  out_root='experiment/cmml_paddle',
                  gpu=0)

runner.train()
runner.test()

或者

python run.py --config configs/cmml.yml --data_root data/COCO --image_root data/COCO/images --out_root experiment/cmml_paddle --gpu 0

Torch Example:

from torchmm import TorchMM

# config: Model running parameters, see configs/
# data_root: Path to dataset
# image_root: Path to images
# cuda: Which gpu to use

runner = TorchMM(config='configs/cmml.yml',
                 data_root='data/COCO', 
                 image_root='data/COCO/images',
                 out_root='experiment/cmml_torch',
                 cuda=0)

runner.train()
runner.test()

或者

python run_torch.py --config configs/cmml.yml --data_root data/COCO --image_root data/COCO/images --out_root experiment/cmml_torch --cuda 0

Model library (Continuously Updating)

Achievement

Multi-Modal papers

  • Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, Hui Xiong, Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach, In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2018) , Ann Arbor, Michigan, USA, 2018.
  • Chen Zhu, Hengshu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, Pan Li, Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning, In ACM Transactions on Management Information Systems (ACM TMIS), 2018.
  • Dazhong Shen, Hengshu Zhu, Chuan Qin, Tong Xu, Enhong Chen, Hui Xiong, Joint Representation Learning with Relation-enhanced Topic Models for Intelligent Job Interview Assessment, In ACM Transactions on Information Systems (ACM TOIS) , 2021.
  • Yang Yang, Jia-Qi Yang, Ran Bao, De-Chuan Zhan, Hengshu Zhu, Xiao-Ru Gao, Hui Xiong, Jian Yang. Corporate Relative Valuation using Heterogeneous Multi-Modal Graph Neural Network. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2021. (CCF-A). Code
  • Yang Yang, De-Chuan Zhan, Yi-Feng Wu, Zhi-Bin Liu, Hui Xiong, and Yuan Jiang. Semi-Supervised Multi-Modal Clustering and Classification with Incomplete Modalities. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2020. (CCF-A)
  • Yang Yang, Chubing Zhang, Yi-Chu Xu, Dianhai Yu, De-Chuan Zhan, Jian Yang. Rethinking Label-Wise Cross-Modal Retrieval from A Semantic Sharing Perspective. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-2021), Montreal, Canada, 2021. (CCF-A).
  • Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang. Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. Proceedings of the Annual Conference on ACM SIGKDD (KDD-2018) , London, UK, 2018. Code

For more papers, welcome to our website njustkmg !

PaddlePaddle Paper Reproduction Competition

  • Paddle Paper Reproduction Competition (4st): "Comprehensive Semi-Supervised Multi-Modal Learning" Championship
  • Paddle Paper Reproduction Competition (5st): "From Recognition to Cognition: Visual Commonsense Reasoning" Championship

Contribution

  • PaddlePaddle reproduce record. link .
  • We welcome you to contribute code to PaddleMM, and thank you very much for your feedback.

License

This project is released under Apache 2.0 license .