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DCAT: A Novel Transformer-Based Approach for Dynamic Context-Aware Image Captioning in the Tamil Language.

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Bibliographic Details
Title: DCAT: A Novel Transformer-Based Approach for Dynamic Context-Aware Image Captioning in the Tamil Language.
Authors: Venugopal, Jothi Prakash, Subramanian, Arul Antran Vijay, Murugan, Manikandan, Sundaram, Gopikrishnan, Rivera, Marco, Wheeler, Patrick
Source: Applied Sciences (2076-3417); May2025, Vol. 15 Issue 9, p4909, 38p
Subject Terms: NATURAL language processing, LOW-resource languages, GENERATIVE pre-trained transformers, TRANSFORMER models, SYNTAX (Grammar)
Abstract: The task of image captioning in low-resource languages like Tamil is fraught with challenges due to limited linguistic resources and complex semantic structures. This paper addresses the problem of generating contextually and linguistically coherent captions in Tamil. We introduce the Dynamic Context-Aware Transformer (DCAT), a novel approach that synergizes the Vision Transformer (ViT) with the Generative Pre-trained Transformer (GPT-3), reinforced by a unique Context Embedding Layer. The DCAT model, tailored for Tamil, innovatively employs dynamic attention mechanisms during its Initialization, Training, and Inference phases to focus on pertinent visual and textual elements. Our method distinctively leverages the nuances of Tamil syntax and semantics, a novelty in the realm of low-resource language image captioning. Comparative evaluations against established models on datasets like Flickr8k, Flickr30k, and MSCOCO reveal DCAT's superiority, with a notable 12% increase in BLEU score (0.7425) and a 15% enhancement in METEOR score (0.4391) over leading models. Despite its computational demands, DCAT sets a new benchmark for image captioning in Tamil, demonstrating potential applicability to other similar languages. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:The task of image captioning in low-resource languages like Tamil is fraught with challenges due to limited linguistic resources and complex semantic structures. This paper addresses the problem of generating contextually and linguistically coherent captions in Tamil. We introduce the Dynamic Context-Aware Transformer (DCAT), a novel approach that synergizes the Vision Transformer (ViT) with the Generative Pre-trained Transformer (GPT-3), reinforced by a unique Context Embedding Layer. The DCAT model, tailored for Tamil, innovatively employs dynamic attention mechanisms during its Initialization, Training, and Inference phases to focus on pertinent visual and textual elements. Our method distinctively leverages the nuances of Tamil syntax and semantics, a novelty in the realm of low-resource language image captioning. Comparative evaluations against established models on datasets like Flickr8k, Flickr30k, and MSCOCO reveal DCAT's superiority, with a notable 12% increase in BLEU score (0.7425) and a 15% enhancement in METEOR score (0.4391) over leading models. Despite its computational demands, DCAT sets a new benchmark for image captioning in Tamil, demonstrating potential applicability to other similar languages. [ABSTRACT FROM AUTHOR]
ISSN:20763417
DOI:10.3390/app15094909