Image Captioning: A Comprehensive Survey

Sharma, Himanshu; Agrahari, Manmohan; Singh, Sujeet Kumar; Firoj, Mohd; Mishra, Ravi

doi:10.1109/parc49193.2020.236619

Cited by 69 publications

(25 citation statements)

References 15 publications

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“…"A query image has been given and they map it into the meaning space by solving Markov Random field and semantic distance between these images are determined by Lin Similarity measure [10] and each existing sentence parsed by Curran and Clark Parser [11]. The caption which is closest to the given image is considered as the caption of the query image" [12]. In order to caption an image V. Ordonez et al [13] firstly used global image descriptors to retrieve set of images from web scale collection of captioned images.…”

Section: Retrieval Based Image Captioning Methodsmentioning

confidence: 99%

Overview on Image Captioning Techniques

2021

IJETER

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Image captioning is a process to assign a meaningful title for a given image with the help of Natural Language Processing (NLP) and Computer Vision techniques. Captioning of an image first need to identify object, attribute and relationship among these in image and second is to generate relevant description for the given image. So it require both NLP and Computer vision techniques to perform image captioning task. Due to complexity of finding relationship between the attribute of the object and its feature makes it a challenging task. Also for machine it is difficult to emulate human brain however researches have shown a prominent achievement in this field and made it easy to solve such problems. The foremost aim of this survey paper is to describe several methods to achieve the same, the core involvement of this paper is to categorise different existing approaches for image captioning, further discussed their subcategories of this method and classify them, also discussed some of their strength and limitations. This survey paper gives theoretical analysis of image captioning methods and defines some earlier and newly approach for image captioning. This survey paper is basically a source of information for researchers in order to get idea of different approaches that were developed so far in the field of image captioning. Key words : Computer Vision, Deep Learning, Neural Network, NLP, Image Captioning, Multimodal Learning.

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Section: Retrieval Based Image Captioning Methodsmentioning

confidence: 99%

Overview on Image Captioning Techniques

2021

IJETER

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“…These deep-learning algorithms are commonly used for ordinal or temporal problems such as language translation [73], natural language processing (NLP) [74], speech recognition [75], and image captioning [76].…”

Section: Lstm Modelmentioning

confidence: 99%

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

et al. 2022

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A significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measurement and prediction results using the system we developed, which was designed to cover beehive ecosystem. It is equipped with an IoT modular base station that collects a wide range of parameters from sensors on the hive and a bee counter at the hive entrance. Data are sent to the cloud for storage, analysis, and alarm generation. A time-series forecasting model capable of estimating the volume of bee exits and entrances per hour, which simulates dependence between environmental conditions and bee activity, was devised. The applied mathematical models based on recurrent neural networks exhibited high accuracy. A web application for monitoring and prediction displays parameters, measured values, and predictive and analytical alarms in real time. The predictive component utilizes artificial intelligence by applying advanced analytical methods to find correlation between sensor data and the behavioral patterns of bees, and to raise alarms should it detect deviations. The analytical component raises an alarm when it detects measured values that lie outside of the predetermined safety limits. Comparisons of the experimental data with the model showed that our model represents the observed processes well.

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“…The selling points can be presented as keywords or short phrases in the form of subtitles, or as a sentence with voice-overs. Existing works in image/video captioning [29,119] and textual selling point mining [41] can be useful starting points.…”

Section: Human Crafted Visual Storylinementioning

confidence: 99%

AI-Empowered Persuasive Video Generation: A Survey

Liu¹,

Han²

2021

Preprint

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Promotional videos are rapidly becoming a popular medium for persuading people to change their behaviours in many settings (e.g., online shopping, social enterprise initiatives). Today, such videos are often produced by professionals, which is a time-, labour-and cost-intensive undertaking. In order to produce such contents to support a large applications (e.g., e-commerce), the field of artificial intelligence (AI)-empowered persuasive video generation (AIPVG) has gained traction in recent years. This field is interdisciplinary in nature, which makes it challenging for new researchers to grasp. Currently, there is no comprehensive survey of AIPVG available. In this paper, we bridge this gap by reviewing key AI techniques that can be utilized to automatically generate persuasive videos. We offer a first-of-its-kind taxonomy which divides AIPVG into three major steps: 1) visual material understanding, which extracts information from the visual materials (VMs) relevant to the target of promotion; 2) visual storyline generation, which shortlists and arranges high-quality VMs into a sequence in order to compose a storyline with persuasive power; and 3) post-production, which involves background music generation and still image animation to enhance viewing experience. We also introduce the evaluation metrics and datasets commonly adopted in the field of AIPVG. We analyze the advantages and disadvantages of the existing works belonging to the above-mentioned steps, and discuss interesting potential future research directions.

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Image Captioning: A Comprehensive Survey

Cited by 69 publications

References 15 publications

Overview on Image Captioning Techniques

Overview on Image Captioning Techniques

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

AI-Empowered Persuasive Video Generation: A Survey

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