Project Background:
Multimodal Transformers represent a significant advancement in artificial intelligence, specifically in understanding and generating content. The project background lies in the desire to develop more comprehensive models capable of seamlessly processing and generating information across different modalities. Traditional transformers have excelled in natural language processing (NLP) tasks, but their extension to handle multimodal data involves addressing unique challenges. It aims to bridge the gap by incorporating textual and visual information into a unified model architecture, leveraging the transformers self-attention mechanism to capture the dependencies and relationships within and between modalities effectively. The motivation behind this endeavor is to enhance the models ability to comprehend and generate content that integrates diverse sources of information and advancing applications in image captioning, visual question answering, and other multimodal tasks.
Problem Statement
- Multimodal Transformers revolves around the need to address the limitations of existing models in effectively processing and generating content that involves multiple modalities.
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The problem is creating a model that can understand the intricate interplay between textual and visual data, capturing complex relationships and dependencies to produce more contextually rich and accurate outputs.
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Challenges include devising mechanisms to fuse information from different modalities handling misalignments between textual and visual content can generalize well across diverse multimodal tasks.
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The goal is to develop a robust and versatile multimodal transformer architecture that overcomes these challenges, enabling more sophisticated interactions between AI systems and multimodal data.
Aim and Objectives
- Develop an advanced multimodal transformer model that seamlessly integrates textual and visual information for improved content understanding and generation.
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Design a transformer architecture capable of handling both textual and visual modalities.
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Implement mechanisms for effective fusion and interaction between textual and visual representations.
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Address challenges related to misalignments and disparities between modalities to enhance model robustness.
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Train the multimodal transformer on diverse datasets to ensure generalization across various tasks.
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Evaluate the models performance on tasks such as image captioning, visual question answering, and other multimodal applications.
Contributions to Multimodal Transformers
- 1. Introduce a novel transformer architecture for multimodal tasks, ensuring efficient fusion and interaction between textual and visual modalities.
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Innovate attention mechanisms tailored to handle multimodal data, addressing challenges related to misalignments and variations between textual and visual content.
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Propose effective training paradigms that leverage large and diverse multimodal datasets, facilitating the generalization of the model across a wide range of tasks essential for the robust performance of multimodal transformers.
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Contribute to the community by curating benchmark datasets specific to multimodal tasks and establishing standardized evaluation metrics for fair and consistent comparison progress tracking in the field.
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Providing an open-source implementation model and comprehensive documentation fosters collaboration, transparency, and reproducibility in developing multimodal AI technologies.
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Conduct thorough experiments and evaluations on standard benchmarks to demonstrate the superior performance of the proposed multimodal transformer compared to existing models.
Deep Learning Algorithms for Multimodal Transformers
- M4C (Multimodal-Transformer with Vision-Language Pre-training)
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LXMERT (Learning Cross-Modality Encoder Representations from Transformers)
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CLIP (Contrastive Language-Image Pre-training)
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DALL-E (Generative Model for Diverse Image Generation)
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UNIT (Unsupervised Image-to-Image Translation)
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BERT-MMT (Multimodal Transformer with BERT Pre-training)
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BERTVision (BERT-based Vision-Language Pre-training)
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B2T2 (Bidirectional Block-transformer for Image Generation)
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ViLBERT (Vision-and-Language BERT)
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VisualBERT (Integrating Visual Information into BERT)
Datasets for Multimodal Transformers
- MS COCO
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Visual Genome
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Conceptual Captions
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Flickr30k
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Hateful Memes Challenge
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VQA (Visual Question Answering) v2.0
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ImageNet
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SNLI-VE (Stanford Natural Language Inference - Visual Entailment)
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CLEVR (Compositional Language and Elementary Visual Reasoning)
Performance Metrics
- Precision
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Recall
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F1 Score
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BLEU (Bilingual Evaluation Understudy) Score
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ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score
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CIDEr (Consensus-based Image Description Evaluation)
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SPICE (Semantic Propositional Image Caption Evaluation)
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WER (Word Error Rate)
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PER (Position-independent word Error Rate)
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FID (Frechet Inception Distance)
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METEOR (Metric for Evaluation of Translation with Explicit ORdering) Score
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
