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OMML mainly based on on PyTorch, compatible with Paddle 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.

Group of OMML:

• NJUST KMG Group

• Add visualization TSNE of fusion task 2023.1.14
• Add model DOMFN 2022.11.17
• Add model CPRC 2022.11.14
• Add model TMC 2022.5.8

more

• 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.

• Sneaker authenticity identification

• more visualization

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

• Characteristic level:
  Example parameter setting: dataset="twitter", data_ mode="twitter", visual="tsne", choose="fusion"
  File output directory: out_ root/tsne
  
  
• Model level:
  
• Indicator level:
  

OMML includes torch version torchmm and paddle version paddlemm packages, 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.

Download the toolkit:

git clone https://github.com/njustkmg/OMML.git

• Data construction instructions here
• Dependent files download here

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()

or

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

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()

or

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

• Modal joint learning - Fusion learning
  • Early (Multi-modal early fusion)
  • Late (Multi-modal late fusion)
  • LMF (Efficient Low-rank Multimodal Fusion with Modality-Specific Factors)
  • TMC (Trusted Multi-View Classification)
• Modal joint learning - Co-training
  • CMML (Comprehensive Semi-Supervised Multi-Modal Learning)
• Cross-modal learning - Modal translation
  • ShowAttendTell (Show, Attend and Tell: Neural Image Caption Generation with Visual Attention)
  • AoANet (Attention on Attention for Image Captioning)
  • CPRC (Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning)
• Cross-modal learning - Modal alignment
  • VSE++ (VSE++: Improving Visual-Semantic Embeddings with Hard Negatives)
  • SCAN (Stacked Cross Attention for Image-Text Matching)
  • BFAN (Focus Your Attention: A Bidirectional Focal Attention Network for Image-Text Matching)
  • IMRAM (IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval)
  • SGRAF (Similarity Reasoning and Filtration for Image-Text Matching)
• Multi-task framework based on Transformer structure
  • VILBERT (ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks)

• 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, Hong-Chen Wei, Heng-Shu Zhu, Dian-Hai Yu, Hui Xiong, Jian Yang. Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning. IEEE Transactions on Cybernetics (IEEE TCYB), 2022 in press. (CCF-B).[Pytorch Code] [Paddle 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
• Yang Yang, Jingshuai Zhang, Fan Gao, Xiaoru Gao, Hengshu Zhu DOMFN: A Divergence-Orientated Multi-Modal Fusion Network for Resume Assessment. ACM MULTIMEDIA(ACMMM-2022) , Lisbon, Portugal, 2022.(CCF-A)
• For more papers, welcome to our website njustkmg !

• 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
• Paddle Paper Reproduction Competition (6st): "Efficient Low-rank Multimodal Fusion with Modality-Specific Factors" Championship

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

This project is released under Apache 2.0 license .