Project Background:
The text-to-image generation models revolve around the intersection of natural language processing (NLP) and computer vision (CV), aiming to bridge the semantic gap between textual descriptions and visual representations. Historically, generating realistic images from textual descriptions has been a challenging task due to the inherent complexity and ambiguity of natural language. However, recent advancements in deep learning, particularly with generative adversarial networks (GANs) and transformer-based models, paved the way for significant progress in this domain. These models learn to translate textual descriptions into corresponding images by capturing and incorporating the underlying semantic information into the image-generation process. It seeks to leverage the advancements to develop more accurate and coherent text-to-image generation models capable of producing high-quality, diverse visual outputs that faithfully represent the input text. By addressing the challenges of understanding and synthesizing complex textual descriptions into visually compelling images, such models hold promise for various applications, including content generation, creative design, and virtual environments.
Problem Statement
- Bridging the semantic gap between textual descriptions and visual representations poses a significant challenge in text-to-image generation.
- Generating realistic and diverse images from textual descriptions requires capturing nuanced semantic information accurately.
- Ambiguity and subjectivity inherent in natural language descriptions complicate the translation process.
- Ensuring coherence and fidelity in generated images remains a key concern.
- The scalability of text-to-image generation models to handle a wide range of textual and diverse image outputs is essential.
- Evaluating the perceptual quality and visual fidelity of generated images objectively.
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Exploring methods to generate images that are not only realistic but also semantically meaningful and contextually appropriate.
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Ensuring interpretability and controllability of the generated images to align with user preferences and requirements.
Aim and Objectives
- Develop robust and effective text-to-image generation models that accurately translate textual descriptions into visually compelling images.
- Improve the semantic understanding of textual descriptions to facilitate more accurate image generation.
- Enhance the diversity and realism of generated images to capture the richness of textual input.
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Optimize computational efficiency to enable scalable and real-time text-to-image generation.
- Develop evaluation metrics and benchmarks to objectively assess the quality and perceptual fidelity of generated images.
- To advance text-to-image generation research foster interdisciplinary collaboration between natural language processing, computer vision, and cognitive science.
Contributions to Text-to-Image Generation Models
- Advancing the semantic understanding of textual descriptions to improve the fidelity of image synthesis.
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Developing methods for controlling the style, composition, and attributes of generated images based on input text.
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Mitigating biases and stereotypes in generated images to promote fairness and diversity.
- Optimizing computational efficiency for scalable and real-time text-to-image generation.
- Establishing evaluation metrics and benchmarks for objective assessment of generated image quality.
- Enabling interpretability and controllability of the text-to-image generation process to align with user preferences.
- Combine text, image, and other modalities for comprehensive synthesis.
- Contributing to applications such as content generation, creative design, and virtual environments through high-quality image synthesis.
Deep Learning Algorithms for Text-to-Image Generation Models
- Generative Adversarial Networks (GANs)
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Variational Autoencoders (VAEs)
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Generative Adversarial Networks with Variational Autoencoders (VAE-GANs)
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Text-to-Image Transformer (T2I-T)
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StackGAN
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StackGAN++
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AttnGAN
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DALL-E
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CLIP-guided Text-to-Image Generation
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Conditional GANs (cGANs)
Datasets for Text-to-Image Generation Models
- MS COCO (Common Objects in Context)
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Visual Genome
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CLEVR (Compositional Language and Elementary Visual Reasoning)
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Oxford-102 Flowers
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Oxford-IIIT Pet
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Caltech-UCSD Birds-200-2011
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WikiArt
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SketchyGAN
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Stanford Online Products
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FashionGen
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
