## 2 Literature Review
[15] developed an end-to-end image captioning model based on reduced feature maps of deep learners pre-trained for object detection, streamlining the captioning process and leading to more efficient image understanding. [PERSON] et al. [16] surveyeddeep learning-based image captioning, offering a comprehensive overview of methodologies and their evolution. Their work presents a valuable compilation of state-of-the-art techniques and directions for future research in the field. [PERSON] et al. [17] introduced a cross-modification attention-based model for image captioning, enhancing the process by dynamically modifying attention mechanisms. This novel approach contributes to a more nuanced understanding and generation of image captions. [PERSON] et al. [18] proposed a transformer-based local-global guidance system for image captioning, an innovative method that improved captioning accuracy by incorporating both local and global contextual cues. [PERSON] et al. [19] developed the MAENet, a multi-head association attention enhancement network, which significantly contributed to completing intra-modal interaction in image captioning. This work enhances the model's ability to understand and describe complex images. [PERSON] et al. [20] introduced the Progressive Tree-Structured Prototype Network for end-to-end image captioning, representing a significant advancement in developing efficient and accurate captioning systems. [PERSON] et al. [21] explored dynamic-balanced double-attention fusion for image captioning, an approach that led to improved accuracy and context-awareness in caption generation. This method balances attention mechanisms in a novel way, enhancing the quality of generated captions. [PERSON] et al. [22] proposed a dynamic convolution-based encoder-decoder framework for image captioning in Hindi, advancing the field in handling low-resource languages. Their method focused on Hindi caption generation and demonstrated the effectiveness of dynamic convolution in image encoding. [PERSON] et al. [23] examined the improvement of low-resource captioning via multi-objective optimization. Their work addressed the unique challenges of captioning in low-resource languages by exploiting both triplet and paired datasets, representing a significant contribution to the field. [PERSON] and [PERSON] [24] developed a deep ensemble network for sentiment analysis in bilingual low-resource languages, addressing the challenges of sentiment analysis in languages like Kannada and Malayalam. This approach fills a significant gap in the field by developing models that can handle the complexities of low-resource languages. [PERSON] et al. [25] focused on detecting mixed social media data with Tamil-English code using machine learning techniques. Their study provided an efficient method to detect hate speech, contributing to the field of automated content moderation and demonstrating the effectiveness of machine learning algorithms in this context. [PERSON] et al. [26] compared supervised and unsupervised learning approaches for Tamil offensive language detection. Their novel work highlighted the effectiveness of machine learning algorithms in detecting offensive language in low-resourced languages, showing that unsupervised clustering can be more effective than human labeling. [PERSON] et al. [27] worked on English to Tamil multi-modal image captioning translation. Their research bridged the gap between English and Tamil, demonstrating the feasibility of cross-lingual image captioning and representing a significant step in the field of multi-modal translation. [PERSON] et al. [28] improved image captioning via enhancing dual-side context awareness. Their approach to integrating different context types led to a significant improvement in caption quality, especially in complex scenes, offering a more nuanced understanding of context in image captioning. [PERSON] et al. [29] developed an image captioning system with adaptive incremental global context attention.