Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial Network

Palash, Aminul Haque; Nasim, Abdullah Al; Dhali, Aditi; Afrin, Faria

doi:10.1109/raaicon54709.2021.9929536

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Although there have been many studies on text-to-image generation in English, very few have been applied to other languages. In [54], the use of Attn-GAN was proposed for generating fine-grained images based on descriptions in Bangla text. It is capable of integrating the most exact details in various subregions of the image, with a specific emphasis on the pertinent terms mentioned in the natural language description.…”

Section: A Text To Image Generation Using Gansmentioning

confidence: 99%

Text-to-Image Synthesis With Generative Models: Methods, Datasets, Performance Metrics, Challenges, and Future Direction

Alhabeeb,

Al-Shargabi

2024

IEEE Access

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Text-to-image synthesis, the process of turning words into images, opens up a world of creative possibilities, and meets the growing need for engaging visual experiences in a world that is becoming more image-based. As machine learning capabilities expanded, the area progressed from simple tools and systems to robust deep learning models that can automatically generate realistic images from textual inputs. Modern, large-scale text-to-image generation models have made significant progress in this direction, producing diversified and high-quality images from text description prompts. Although several methods exist, Generative Adversarial Networks (GANs) have long held a position of prominence. However, diffusion models have recently emerged, with results much beyond those achieved by GANs. This study offers a concise overview of text-to-image generative models by examining the existing body of literature and provide a deeper understanding of this topic. This will be accomplished by providing a concise summary of the development of text-to-image synthesis, previous tools and systems employed in this field, key types of generative models, as well as an exploration of the relevant research conducted on GANs and diffusion models. Additionally, the study provides an overview of common datasets utilized for training the text-toimage model, compare the evaluation metrics used for evaluating the models and addresses the challenges encountered in the field. Finally, concluding remarks are provided to summarize the findings and implications of the study and open issues for further research.

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Section: A Text To Image Generation Using Gansmentioning

confidence: 99%

Text-to-Image Synthesis With Generative Models: Methods, Datasets, Performance Metrics, Challenges, and Future Direction

Alhabeeb,

Al-Shargabi

2024

IEEE Access

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“…Attentional Generative Adversarial Network (AttnGAN) enables multi-stage, attention-driven image generation from textual description [7], [8]. AttnGAN begins with a rudimentary low-resolution image which it then refines in multiple phases to produce a final image from the natural language description.…”

Section: A Gansmentioning

confidence: 99%

“…Their proposed model showed substantial improvements in Text to image synthesis. Later on, the paper [7] suggested the first Bangla language-based Text-to-image generation method AttnGAN that analyzed Deep Attentional Multimodal Similarity Model and Attentional GAN to generate improved and realistic highresolution images from Bangla text description surpassing the state-of-the-art (SOTA) image synthesis GAN models by an ideal inception score of 3.58 ± .06.…”

Section: B Text To Image Synthesismentioning

confidence: 99%

Facial Image Generation from Bangla Textual Description using DCGAN and Bangla FastText

Arnob,

Rahman,

Mahmud

et al. 2023

IJACSA

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The synthesis of facial images from textual descriptions is a relatively difficult subfield of text-to-image synthesis. It is applicable in various domains like Forensic Science, Game Development, Animation, Digital Marketing, and Metaverse. However, no work was found that generates facial images from textual descriptions in Bangla; the 5th most spoken language in the world. This research introduces the first-ever system to generate facial images from Bangla textual descriptions. The proposed model comprises two fundamental constituents, namely a textual encoder, and a Generative Adversarial Network(GAN). The text encoder is a pre-trained Bangla text encoder named Bangla FastText which is employed to transform Bangla text into a latent vector representation. The utilization of Deep Convolutional GAN (DCGAN) allows for the generation of face images that correspond to text embedding. Furthermore, a Bangla version of the CelebA dataset, CelebA Bangla is created for this study to develop the proposed system. CelebA Bangla contains images of celebrities, their corresponding annotated Bangla facial attributes and Bangla Textual Descriptions generated using a novel description generation algorithm. The proposed system attained a Fréchet Inception Distance (FID) score of 126.708, Inception Score(IS) of 12.361, and Face Semantic Distance(FSD) of 20.23. The novel text embedding strategy used in this study outperforms prior work. A thorough qualitative and quantitative analysis demonstrates the superior performance of the proposed system over other experimental systems.

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“…Reed et al [8] suggested a method of translating singlesentenced text descriptions directly into image pixels by introducing a deep convolutional GAN based on text description embedding compressed using a fully connected layer and leaky-ReLU activation and text features used to perform feedforward inference by the generator and discriminator network fundamentally demonstrating enhanced text to image synthesis. In Paper [9], they proposed the use of the Bangla Attentional Generative Adversarial Network (AttnGAN) to generate highquality images from texts through multi-staged processing and incorporation of specific details in distinct parts of images, achieving an enhanced inception score on the CUB dataset.…”

Section: A Text To Image Generationmentioning

confidence: 99%

Mukh-Oboyob: Stable Diffusion and BanglaBERT enhanced Bangla Text-to-Face Synthesis

Saha,

Arnob,

Rahman

et al. 2023

IJACSA

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Facial image generation from textual generation is one of the most complicated tasks within the broader topic of Text-to-Image (TTI) synthesis. It is relevant in several fields of scientific research, cartoon and animation development, online marketing, game development, etc. There have been extensive studies on Text-to-Face (TTF) synthesis in the English language. However, the amount of relevant existing work in Bangla is limited and not comprehensive. As the TTF field is not vastly prospected for Bangla language, the objective of this study sets forth to explore the possibilities in the field of Bangla Natural Language Processing and Computer Vision. In this paper, a novel system for generating highly detailed facial images from textual descriptions in the Bangla language is proposed. The proposed system named Mukh-Oboyob consists of two essential components: a pre-trained language model, BanglaBERT, and Stable Diffusion. BanglaBERT, a transformer-based pre-trained text encoder, is a language model used to transform Bangla sentences into vector representations. Stable Diffusion is used by Mukh-Oboyob to generate facial images utilizing the text embedding of the Bangla sentences. Moreover, the work utilizes CelebA Bangla, a modified version of the CelebA dataset consisting of face images, Bangla facial attributes, and Bangla text descriptions to develop and train the proposed system. This paper establishes a system for image synthesis with excellent performance and detailed image outcomes, as evidenced by a comprehensive analysis incorporating both qualitative and quantitative measures, leading to the system under consideration achieving an impressive FID score of 34.6828 and an LPIPS score of 0.4541.

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Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial Network

Cited by 7 publications

References 15 publications

Text-to-Image Synthesis With Generative Models: Methods, Datasets, Performance Metrics, Challenges, and Future Direction

Text-to-Image Synthesis With Generative Models: Methods, Datasets, Performance Metrics, Challenges, and Future Direction

Facial Image Generation from Bangla Textual Description using DCGAN and Bangla FastText

Mukh-Oboyob: Stable Diffusion and BanglaBERT enhanced Bangla Text-to-Face Synthesis

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