Project Background:
Multimodal Question Answering (MQA) is rooted in the increasing demand for artificial intelligence (AI) systems capable of comprehending and responding to queries that involve textual and visual information. Traditional question-answering systems primarily focused on textual data, such as images and text, have surged in a critical need for models to understand and generate answers by jointly considering diverse modalities. This challenge lies in developing a robust architecture that seamlessly integrates information from text and images, allowing for a more comprehensive understanding. The background of this project involves addressing the complexities associated with cross-modal reasoning, handling language and vision fusion, and creating models that can generalize well across diverse domains. By delving into the intricacies of multimodal interactions, the project aims to push the boundaries of question-answering systems, enabling to provide more contextually rich and accurate responses across a broad spectrum of real-world applications.
Problem Statement
- The problem statement in MQA revolves around the challenges of effectively integrating information from textual and visual modalities to generate accurate and coherent answers.
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The primary issues include devising mechanisms to align and fuse information from different sources, addressing semantic gaps between textual and visual data overcoming the inherent complexities of cross-modal reasoning.
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Furthermore, handling ambiguous queries and ensuring the MQA system can generalize across diverse datasets and domains are critical aspects of the problem.
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The goal is to create robust models that can effectively bridge the gap between language and vision, providing accurate and contextually relevant answers to questions involving textual and visual information.
Aim and Objectives
- Develop an advanced MQA system that seamlessly integrates textual and visual information for accurate and contextually rich responses.
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Design a multimodal architecture capable of processing both textual and visual inputs.
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Implement mechanisms for effective fusion and interaction between textual and visual representations.
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Address challenges related to cross-modal reasoning and semantic alignment.
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Create a model that can generalize well across diverse datasets and domains.
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Evaluate the MQA system performance on benchmark datasets for text- and image-based questions.
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Fine-tune the model to handle ambiguous queries and improve overall robustness.
Contributions to Multimodal Question Answering
1. Propose a novel multimodal architecture tailored for MQA that effectively integrates textual and visual information, providing a seamless framework for cross-modal reasoning.
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Innovate attention mechanisms specific to MQA, addressing challenges related to aligning and fusing information from diverse modalities to focus on relevant details.
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Develop methodologies to handle semantic misalignments between textual and visual data, ensuring accurate question comprehension and generating contextually relevant answers.
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Curate benchmark datasets specifically tailored for MQA and standardized evaluation metrics, fostering fair and consistent comparisons between different models and approaches.
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Introduce strategies to handle ambiguous queries effectively, enabling the MQA system to provide meaningful responses even in cases where questions may have multiple interpretations.
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Explore methods to incorporate user interaction and feedback into the MQA system, enhancing its adaptability and responsiveness to user needs over time.
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Contribute to the broader field of multimodal AI by pushing the boundaries of MQA to the overall understanding and development of systems capable of handling complex multimodal questions.
Deep Learning Algorithms for Multimodal Question Answering
- ViLBERT (Vision-and-Language BERT)
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CLIP (Contrastive Language-Image Pre-training)
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BERT-VQA (BERT-based Visual Question Answering)
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FUSION-DCN (Fusion with Dynamic Co-Attention Networks)
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Hetero-MultiHop (Heterogeneous Multi-Hop Reasoning)
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MM-BERT (Multimodal BERT)
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VL-BERT (Visual-Linguistic BERT)
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Unicoder-VQA (Unified Visual-Language Pre-training for Vision-Language Understanding)
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LXMERT (Learning Cross-Modality Encoder Representations from Transformers)
Datasets for Multimodal Question Answering
- VQA (Visual Question Answering)
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OK-VQA (Okayama-VQA)
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GQA (Visual Question Answering in Real-world Scenes)
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VizWiz (Visual Question Answering in the Wild)
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SNLI-VE (Stanford Natural Language Inference - Visual Entailment)
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NLVR2 (Natural Language Visual Reasoning)
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HINT (Hierarchy-Inducing Text-Image Matching Dataset)
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TVQA (TV Question Answering)
Performance Metrics
- Accuracy
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Precision
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Recall
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F1 Score
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MAP (Mean Average Precision)
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BLEU (Bilingual Evaluation Understudy)
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METEOR (Metric for Evaluation of Translation with Explicit ORdering)
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CIDEr (Consensus-based Image Description Evaluation)
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ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
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SPICE (Semantic Propositional Image Caption Evaluation)
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
