Project Background:
Multimodal Language Grounding in Vision is at the intersection of computer vision and natural language processing (NLP). The primary objective of this project is to bridge the gap between textual descriptions and visual content, enabling machines to understand and interpret the meaning conveyed in both modalities. This project addresses the challenge of grounding language visually, aiming to develop models that can associate words and phrases with corresponding elements in images or videos. It involves deeply exploring neural network architectures, attention mechanisms, and multimodal fusion techniques to enhance linguistic and visual information synergy. The ultimate goal is to empower machines to comprehend natural language instructions in the context of visual stimuli, leading to advancements in tasks such as image captioning, visual question answering, and overall improved human-machine communication. As the boundaries between disciplines blur, multimodal language grounding in vision stands at the forefront of research, pushing the boundaries of AI to create more sophisticated and intuitive systems that can seamlessly interpret the richness of both linguistic and visual data.
Problem Statement
- In this project, the multimodal language grounding in vision lies in developing models that connect natural language descriptions with visual content.
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This challenge involves addressing the ambiguity in language, navigating the complexity of visual scenes, and designing models capable of extracting and fusing information from both modalities.
- Additionally, handling diverse data and ensuring generalization across different scenarios are key aspects of the problem.
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The ultimate goal is to create intelligent systems to understand and interpret the rich meanings conveyed in language and vision for applications like image captioning and visual question answering.
Aim and Objectives
- Develop AI models to seamlessly integrate natural language descriptions with visual content to enhance the understanding of complex scenes.
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Achieve precise semantic alignment between language and visual elements.
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Enable models to comprehend and navigate intricate visual scenes.
Design effective mechanisms for feature extraction and fusion of linguistic and visual information.
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Ensure model versatility through diversity handling and generalization across scenarios.
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Investigate transfer learning and domain adaptation for real-world applicability.
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Apply developed models to tasks like image captioning and visual question answering for improved human-computer interaction.
Contributions to Multimodal Language Grounding in Vision
- 1. To propose innovative approaches for precise semantic alignment between natural language expressions and corresponding visual elements.
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Advancing models to navigate and understand the complexities of diverse visual scenes effectively.
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To Introduce novel mechanisms for extracting and fusing features from linguistic and visual modalities, enhancing overall comprehension.
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To contribute methods to handle diverse datasets, ensuring robust performance and generalization across different scenarios and languages.
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To provide insights into effective transfer learning and domain adaptation strategies, enhancing the adaptability of models to real-world applications.
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To apply developed models to tasks such as image captioning and visual question answering, contributing to improved human-computer interaction and the broader field of multimodal AI.
Deep Learning Algorithms for Multimodal Language Grounding in Vision
- Long Short-Term Memory (LSTM) Networks
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Gated Recurrent Units (GRU)
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Attention Mechanisms
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CNN-LSTM Hybrid Models
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BERT-based Vision-Language Models
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Image Captioning Networks
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Multimodal Fusion Networks
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Neural Architecture Search (NAS) for Multimodal Learning
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Visual Semantic Role Labeling Models
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Cross-Modal Retrieval Networks
Datasets for Multimodal Language Grounding in Vision
- MSCOCO (Microsoft Common Objects in Context)
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Visual Genome
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Flickr30k
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ADE20K (MIT ADE20K Scene Parsing)
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COCO-Text
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SBU Captioned Photo Dataset
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ReferItGame Dataset
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Multi30K
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IMDb-WIKI - Face Images with Age and Gender Labels
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ActivityNet Captions
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Charades
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YouCook2
Performance Metrics
- BLEU
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METEOR
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CIDEr
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ROUGE
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WMD (Word Movers Distance)
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Flicker8k and MSCOCO Image Captioning Evaluation Metrics
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Precision, Recall, and F1 Score for Object Detection
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Accuracy for Image Classification
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Rank Correlation Metrics for Cross-Modal Retrieval
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Perplexity for Language Modeling
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Spearman Rank Correlation for Semantic Similarity
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Intersection over Union (IoU) for Object Localization
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
