Generating Caption for Image using Beam Search and Analyzation with Unsupervised Image Captioning Algorithm

Shambharkar, Prashant Giridhar; Kumari, Priyanka; Yadav, Pratik; Kumar, Rajat

doi:10.1109/iciccs51141.2021.9432245

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…With a forget gate, LSTM can keep relevant information throughout the processing of inputs while discarding non-relevant information. It can process not only single data points but also complete data sequences [26].…”

Section: The Decoder Modelmentioning

confidence: 99%

A Comparison between Vgg16 and Xception Models used as Encoders for Image Captioning

Almogbil¹,

Alghamdi²,

Alsahli³

et al. 2022

Computer Science and Information Technology

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Image captioning is an intriguing topic in Natural Language Processing (NLP) and Computer Vision (CV). The present state of image captioning models allows it to be utilized for valuable tasks, but it demands a lot of computational power and storage memory space. Despite this problem's importance, only a few studies have looked into models’ comparison in order to prepare them for use on mobile devices. Furthermore, most of these studies focus on the decoder part in an encoder-decoder architecture, usually the encoder takes up the majority of the space. This study provides a brief overview of image captioning advancements over the last five years and illustrate the prevalent techniques in image captioning and summarize the results. This research study also discussed the commonly used models, the VGG16 and Xception, while using the Long short-term memory (LSTM) for the text generation. Further, the study was conducted on the Flickr8k dataset.

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Section: The Decoder Modelmentioning

confidence: 99%

A Comparison between Vgg16 and Xception Models used as Encoders for Image Captioning

Almogbil¹,

Alghamdi²,

Alsahli³

et al. 2022

Computer Science and Information Technology

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show abstract

“…These solutions, whether they rely on transformers and attention mechanisms [6,7,8], or scene graphs as presented in [9], in which learning is supervised, or relying on beam search analysis or gated recurrent units (GRU) units, in which learning is unsupervised [10,11], generate one single sentence for each input image. Such models are trained on RGB image datasets [12,13].…”

Section: Sentence Captioningmentioning

confidence: 99%

Egocentric Scene Description for the Blind and Visually Impaired

Delloul

Larabi

2022

2022 5th International Symposium on Informatics and Its Applications (ISIA)

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In recent years, image captioning and segmentation have emerged as crucial tasks in computer vision, with applications ranging from autonomous driving to content analysis. Although multiple solutions have emerged to help blind and visually impaired people move around their environment, few are applications that help them understand and rebuild a scene in their minds through text. Most built models focus on helping users move and avoid obstacles, restricting the number of environments blind and visually impaired people can be in.In this paper, we will propose an approach that helps them understand their surroundings using image captioning. The particularity of our research is that we offer them descriptions with positions of regions and objects regarding them (left, right, front), as well as positional relationships between regions, while we aim to give them access to theatre plays by applying the solution to our TS-RGBD dataset.

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