Image Caption Generation Research Articles

A comprehensive review of image caption generation

A comprehensive review of image caption generation

Diffusion-Cap: A diffusion model for image captioning

Abstract While autoregressive image caption generation models have achieved remarkable success, they are still constrained in generation speed, which may become a bottleneck in practical applications. To address this challenge, our research introduces the Diffusion Image Captioning Model (Diffusion-Cap). This innovative, non-autoregressive framework conceptualizes image captioning as a process that unifies continuous and discrete diffusion. By exploring two distinct architectural designs, our research investigates optimal strategies for the image captioning diffusion process. Comprehensive assessments on the MSCOCO dataset confirm the superior performance of our Diffusion-Cap, surpassing existing non-autoregressive benchmarks in crafting accurate captions.

Hybrid explainable image caption generation using image processing and natural language processing

Hybrid explainable image caption generation using image processing and natural language processing

IMAGE CAPTION GENERATOR USING CNN AND LSTM

Machine learning is now all the rage in the AI world. We have recently used AI to construct very clever devices with exceptional performance. Deep learning is a subset of machine learning that produces very accurate findings, which in turn indicates very good performance. Apps for picture description make use of deep learning in our study. Providing a description of a picture's content is what image description is all about. Object and action detection in the input picture is the foundation of the notion. When describing images, there are primarily two methods: bottom-up and top-down. Bottom-up methods create captions by combining the information of an input picture. Using different architectures, such as recurrent neural networks, top-down methods provide a semantic representation of an input picture, which is then translated into a caption. One potential advantage of picture description is that it might aid those with visual impairments in comprehending what is shown in online images. What follows is an explanation of the specifics. Looking at the image below, what can you make out?

Image Caption Generator Using CNN and LSTM

In this have a look at, we discover the integration of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the motive of image caption generation, a mission that involves a fusion of herbal language processing and computer imaginative and prescient techniques to describe images in English. Delving into the realm of photograph captioning, we meticulously investigate several fundamental concepts and methodologies associated with this area. Our technique includes leveraging prominent equipment inclusive of the Keras library, numpy, and Jupyter notebooks to facilitate the development of our studies. Furthermore, we delve into the utilization of the flickr_dataset and CNNs for image category, elucidating their significance in our examination. Through this research endeavor, we aim to make a contribution to the development of image captioning structures with the aid of combining modern-day strategies from both laptop imaginative and prescient and herbal language processing domain names.

Enhancing image caption generation through context-aware attention mechanism

Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research has focused on caption generation for English, addressing low-resource languages like Bengali presents challenges, particularly in producing coherent captions linking visual objects with corresponding words. This paper proposes a context-aware attention mechanism over semantic attention to accurately diagnose objects for image captioning in Bengali. The proposed architecture consists of an encoder and a decoder block. We chose ResNet-50 over the other pre-trained models for encoding the image features due to its ability to solve the vanishing gradient problem and recognize complex object features. For decoding generated captions, a bidirectional Gated Recurrent Unit (GRU) architecture combined with an attention mechanism captures contextual dependencies in both directions, resulting in more accurate captions. The paper also highlights the challenge of transferring knowledge between domains, especially with culturally specific images. Evaluation of three Bengali benchmark datasets, namely BAN-Cap, BanglaLekhaImageCaption, and Bornon, demonstrates significant performance improvement in METEOR score over existing methods by approximately 30%, 18%, and 45%, respectively. The proposed context-aware, attention-based image captioning system significantly outperforms current state-of-the-art models in Bengali caption generation despite limitations in reference captions on certain datasets.

Automatic Audio and Image Caption Generation with Deep Learning

A novel approach to image caption generation tailored specifically for visually impaired individuals. The proposed system employs advanced computer vision algorithms to analyze images and generate descriptive textual captions. Furthermore, it integrates seamless text-to-speech conversion functionality, allowing for the automatic transformation of these captions into spoken audio, thereby enabling access to visual content for individuals with visual impairments. The goal of this project is to generate descriptive captions for a given photograph or image. We achieve this by employing Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, both of which are advanced deep learning techniques. Using computer vision, the system identifies the content of the image and generates a relevant caption. This caption is then converted into audio using Natural Language Processing (NLP).

A transformer-based Urdu image caption generation

Image caption generation has emerged as a remarkable development that bridges the gap between Natural Language Processing (NLP) and Computer Vision (CV). It lies at the intersection of these fields and presents unique challenges, particularly when dealing with low-resource languages such as Urdu. Limited research on basic Urdu language understanding necessitates further exploration in this domain. In this study, we propose three Seq2Seq-based architectures specifically tailored for Urdu image caption generation. Our approach involves leveraging transformer models to generate captions in Urdu, a significantly more challenging task than English. To facilitate the training and evaluation of our models, we created an Urdu-translated subset of the flickr8k dataset, which contains images featuring dogs in action accompanied by corresponding Urdu captions. Our designed models encompassed a deep learning-based approach, utilizing three different architectures: Convolutional Neural Network (CNN) + Long Short-term Memory (LSTM) with Soft attention employing word2Vec embeddings, CNN+Transformer, and Vit+Roberta models. Experimental results demonstrate that our proposed model outperforms existing state-of-the-art approaches, achieving 86 BLEU-1 and 90 BERT-F1 scores. The generated Urdu image captions exhibit syntactic, contextual, and semantic correctness. Our study highlights the inherent challenges associated with retraining models on low-resource languages. Our findings highlight the potential of pre-trained models for facilitating the development of NLP and CV applications in low-resource language settings.

Enhancing Image Captioning Using Deep Convolutional Generative Adversarial Networks

Introduction:: Introduction: Image caption generation has long been a fundamental challenge in the area of computer vision (CV) and natural language processing (NLP). In this research, we present an innovative approach that harnesses the power of Deep Convolutional Generative Adversarial Networks (DCGAN) and adversarial training to revolutionize the generation of natural and contextually relevant image captions. Method:: Our method significantly improves the fluency, coherence, and contextual relevance of generated captions and showcases the effectiveness of RL reward-based fine-tuning. Through a comprehensive evaluation of COCO datasets, our model demonstrates superior performance over baseline and state-of-the-art methods. On the COCO dataset, our model outperforms current state-of-the-art (SOTA) models across all metrics, achieving BLEU-4 (0.327), METEOR (0.249), Rough (0.525) and CIDEr (1.155) scores. Result:: The integration of DCGAN and adversarial training opens new possibilities in image captioning, with applications spanning from automated content generation to enhanced accessibility solutions. Conclusion:: This research paves the way for more intelligent and context-aware image understanding systems, promising exciting future exploration and innovation prospects.

Chinese image captioning with fusion encoder and visual keyword search

AbstractAutomatic generation of image captions is essentially a cross‐modal conversion from image to text. Owing to the differences in linguistic characteristics between Chinese and English, quite a few Chinese image captioning methods have recently been proposed. Nevertheless, the existing Chinese image captioning models usually lack attention to local details of images or tend to produce general descriptions. To address these challenges, a Chinese image captioning method is proposed that incorporates fusion encoder, visual keyword search, and reinforcement learning. The fusion encoder can simultaneously extract local and global features of the input image to enrich the semantic information in the decoding stage, visual keyword search can pursue potential visual words associated with the image content, and the reinforcement learning mechanism can optimize the evaluation metric CIDEr at sentence level to promote the lexical diversity of image description. The results of extensive experiments demonstrate that the proposed model outperforms the state‐of‐the‐art models and delivers expressive and informative Chinese image captions.

Image Caption Prediction Using Deep Learning

Abstract: In the era of rapidly growing digital content, the automatic generation of image captions has gain significant attention in the field of deep learning and computer vision. Our proposed model leverages deep learning techniques, including convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) with attention mechanisms for caption generation. To evaluate the performance of our model, we employ a comprehensive dataset containing a diverse range of images and corresponding human-generated captions. In conclusion, our deep learning-based image captioning project offers a promising solution to the challenge of automatically generating meaningful and contextually accurate descriptions for images. The potential applications of the technology are vast, and we anticipate that it will continue to various fields, including artificial intelligence, accessibility, content creation, content management and publishing across various social media platform.

Image Caption Generator

An Image Caption Generator is a sophisticated AI system that combines computer vision and natural language processing to automatically create descriptive textual captions for images. This technology utilizes deep learning, particularly Convolutional Neural Networks (CNNs), to analyze and extract meaningful visual features from the input image. These features capture details about the objects, scenes, and elements within the image. Subsequently, a natural language processing model, often built on Recurrent Neural Networks (RNNs) or Transformers, processes these visual features and generates coherent, contextually relevant captions. Post-processing steps may be applied to enhance the quality of the generated text. The primary aim of Image Caption Generators is to facilitate image understanding, improve accessibility, and enhance content search ability by providing human-readable descriptions for visual content. This technology is instrumental in various fields, including content tagging, accessibility tools for the visually impaired, and enhancing user experiences in multimedia content management systems, ultimately bridging the gap between visual and textual information for a more comprehensive and humanlike interpretation of images

Research on image caption generation method based on multi-modal pre-training model and text mixup optimization

Research on image caption generation method based on multi-modal pre-training model and text mixup optimization

A Comparative Study of Feature Extraction Models for Image Caption Generation

A Comparative Study of Feature Extraction Models for Image Caption Generation

Image Caption Generator using Deep Learning

Image Caption Generation has always been a study of great interest to the researchers in the Artificial Intelligence department. Being able to program a machine to accurately describe an image or an environment like an average human has major applications in the field of robotic vision, business and many more. Automatic caption generation with attention mechanisms aims at generating more descriptive captions containing coarse to fine semantic contents in the image. This has been a challenging task in the field of artificial intelligence. In this paper, we present different image caption generating models based on deep neural networks, focusing on the various CNN techniques and analyzing their influence on the sentence generation. We have also generated captions for sample images and compared the different feature extraction and encoder models to analyse which model gives better accuracy and generates the desired results

Detection and Caption Generation of Image Using Deep Learning

In this research paper, we employ a combination of CNN and LSTM to tackle image caption generation, a task at the intersection of natural language processing and computer vision. We meticulously discuss key concepts in photograph captioning and its methodologies, leveraging resources like the Keras library, numpy, and Jupyter notebooks. Our research also delves into the flickr_dataset and CNN for photo classification, aiming to shed light on the intricate processes underlying image understanding and caption generation. Keywords- CNN ,LSTM, Image captioning, Deep Learning.

DeepLens: Integrating Deep Learning for Image Captioning and Hashtag Generation

In this research, we introduce a groundbreaking methodology that leverages deep learning techniques to revolutionize the process of generating descriptive captions and hashtags for images, effectively bridging the gap between computer vision and natural language understanding. Traditional approaches to image caption generation have often relied on rudimentary techniques such as handcrafted features and rule-based systems, which inherently struggle to capture the intricate semantics of images and adapt to diverse datasets. Recognizing these limitations, our novel framework integrates state-of-the-art convolutional neural networks (CNNs) for precise image feature extraction and recurrent neural networks (RNNs), specifically employing the ResNet-50 architecture, for seamless sequence generation. Furthermore, in addition to traditional evaluation metrics such as BLEU scores and human assessments, our system, DeepLens, introduces a groundbreaking feature: automatic hashtag generation. By meticulously analyzing both the content and context of images, DeepLens autonomously generates hashtags that enrich social media content sharing and engagement, presenting a novel paradigm in the realm of image captioning. In addition to its advancements in captioning accuracy and user experience enhancement, DeepLens offers scalability and adaptability to various domains. Its robust architecture allows for seamless integration with different datasets and environments, making it versatile for a wide range of applications. Through this comprehensive approach, we aim to not only enhance the accuracy and relevance of generated captions but also elevate the overall user experience in navigating and interacting with visual content across various platforms and applications.

Exploring a Spectrum of Deep Learning Models for Automated Image Captioning: A Comprehensive Survey

Automatic caption generation from images has emerged as a fundamental and challenging problem at the intersection of computer vision and natural language processing. This paper presents a comprehensive survey of the techniques, methodologies, and advancements in the field of automatic caption generation from images. The primary objective is to provide an extensive review of the state-of-the-art models, evaluation metrics, datasets, and applications associated with this domain. The survey begins by elucidating the underlying principles of image feature extraction and caption generation. Various neural network architectures, including Convolutional Neural Networks (CNNs) and recurrent models such as Long Short-Term Memory (LSTM) networks, are discussed in detail. Additionally, the paper explores the integration of attention mechanisms and reinforcement learning strategies to enhance the quality and relevance of generated captions. A thorough examination of evaluation metrics, encompassing both automated and human-centric approaches, is presented to evaluate the generated captions quantitatively and qualitatively. The survey also highlights prominent datasets that have significantly contributed to the advancement of research in this field, facilitating a deeper understanding of challenges and trends. Furthermore, the paper discusses practical applications and real-world use cases where automatic caption generation plays a pivotal role, including accessibility, multimedia indexing, and assistive technologies. The discussion concludes by outlining open challenges and future directions, aiming to inspire further research and innovation in automatic caption generation from images. The aim of this paper is to examine and contrast diverse end-to-end learning frameworks for image captioning, employing established evaluation metrics to comprehend their applicability across different research domains. In addition to the comparative analysis, the paper addresses future challenges in this domain.

Image Captioning: A Comprehensive Review

Abstract: The Image Caption Generator is an intriguing project that bridges the domains of computer vision and natural language processing, aiming to automatically generate descriptive text for images. In the last decade, considerable progress has been made, yet key challenges in optimising Convolutional Neural Networks (CNNs) for precise image feature extraction and refining Long Short-Term Memory (LSTM) networks for coherent text generation persist. Challenges include overfitting, limited context understanding, and difficulties in modelling long-range dependencies. Solutions have emerged in the form of attention mechanisms, which help focus on relevant image regions, and advanced LSTM architectures like the use of Gated Recurrent Units (GRUs) to improve sequential modelling. These innovations have significantly enhanced the overall performance, leading to more accurate and contextually relevant image captions, ultimately advancing the synergy between computer vision and natural language processing in the realm of image captioning

DIC-Transformer: interpretation of plant disease classification results using image caption generation technology

Disease image classification systems play a crucial role in identifying disease categories in the field of agricultural diseases. However, current plant disease image classification methods can only predict the disease category and do not offer explanations for the characteristics of the predicted disease images. Due to the current situation, this paper employed image description generation technology to produce distinct descriptions for different plant disease categories. A two-stage model called DIC-Transformer, which encompasses three tasks (detection, interpretation, and classification), was proposed. In the first stage, Faster R-CNN was utilized to detect the diseased area and generate the feature vector of the diseased image, with the Swin Transformer as the backbone. In the second stage, the model utilized the Transformer to generate image captions. It then generated the image feature vector, which is weighted by text features, to improve the performance of image classification in the subsequent classification decoder. Additionally, a dataset containing text and visualizations for agricultural diseases (ADCG-18) was compiled. The dataset contains images of 18 diseases and descriptive information about their characteristics. Then, using the ADCG-18, the DIC-Transformer was compared to 11 existing classical caption generation methods and 10 image classification models. The evaluation indicators for captions include Bleu1–4, CiderD, and Rouge. The values of BLEU-1, CIDEr-D, and ROUGE were 0.756, 450.51, and 0.721. The results of DIC-Transformer were 0.01, 29.55, and 0.014 higher than those of the highest-performing comparison model, Fc. The classification evaluation metrics include accuracy, recall, and F1 score, with accuracy at 0.854, recall at 0.854, and F1 score at 0.853. The results of DIC-Transformer were 0.024, 0.078, and 0.075 higher than those of the highest-performing comparison model, MobileNetV2. The results indicate that the DIC-Transformer outperforms other comparison models in classification and caption generation.