A Comparative Study of Generative Adversarial Networks for Text-to-Image Synthesis

Chopra, Muskaan; Singh, Sunil K.; Sharma, Anil; Gill, Shabeg Singh

doi:10.4018/ijssci.300364

Cited by 25 publications

(15 citation statements)

References 11 publications

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“…The primary part involves acquisition of pictures either through smart devices [11,16,21] such as camera and mobile or from internet. The second part segments the image [5,15] into varied numbers of clusters that completely different techniques will be applied. Next part contains feature extraction strategies and therefore the last part is regarding the classification of diseases.…”

Section: Proposed Methodologymentioning

confidence: 99%

Plant Disease Detection using Image Processing

Mishra

Chaurasia²,

Arya³

et al. 2023

Lecture Notes in Networks and Systems

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Plant diseases have an impact on the development of their particular species, hence early detection is crucial. Numerous Machine Learning (ML) models have been used for the identification and classification of plant diseases, this field of study now appears to have significant potential for improved accuracy. In order to identify and categorise the signs of plant diseases, numerous developed/modified ML architectures are used in conjunction with a number of visualisation techniques. Additionally, a number of performance indicators are employed to assess these structures and methodologies.

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Section: Proposed Methodologymentioning

confidence: 99%

Plant Disease Detection using Image Processing

Mishra

Chaurasia²,

Arya³

et al. 2023

Lecture Notes in Networks and Systems

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“…In the previous research works on processing of images with moving objects, the first problem to be solved was to remove the blurring appearance or other noise of the moving objects in the video Sagawa et al, 2009;Lu, 2018), (Qian et al, 2022;Chu et al, 2022;Zheng et al, 2022;Chopra et al, 2022;Li et al, 2019) (Almomani et al, 2022). Most of these methods are valid for tracking objects with normal size (Lee, 2015;Xiao et al, 2015;Al-Jilani et al, 2019).…”

Section: Figure 1 Example Of Table Tennis Ball To Be Trackedmentioning

confidence: 99%

A Semantic Tree-Based Fast-Moving Object Trajectory Tracking Algorithm for Table Tennis

Jin,

Zou,

Sun

et al. 2024

International Journal on Semantic Web and Information Systems

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Table tennis is a popular sport around the world. A key technology in table tennis education and analysis system is reconstructing the trajectory of the fast-moving ball from videos. Typically the table tennis ball is too small and barely visible in the video, making it difficult to be recognized directly by detection models like YOLO. However, table tennis balls usually has obvious motion features, which are usually not found in similar false targets. It inspired the authors to first find all candidate targets and then use the motion features of table tennis ball to select them out. In this article, the authors propose a tree-based algorithm named T-FORT to track the ball and reconstruct its trajectory. Specifically, they consider all the possible objects in a tree-framework, and identify the real target by integrating visual features and moving patterns. The authors conduct a set of experiments on three datasets to evaluate the effectiveness and performance of the proposed algorithm. The experimental results show that the proposed method is more precise than existing algorithms, and is robust in various scenarios.

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“…The proposed strategy can be applied to other text-to-image synthesis methods. Chopra et al [19] provide an overview of current advances in the field of image synthesis from text descriptions. The authors study various architectures of generative models designed to solve the problem of image synthesis from a text description.…”

Section: Introductionmentioning

confidence: 99%

Generating images using generative adversarial networks based on text descriptions

Turarova,

Bekbayeva,

Abdykerimova

et al. 2024

IJECE

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Modern developments in the fields of natural language processing (NLP) and computer vision (CV) emphasize the increasing importance of generating images from text descriptions. The presented article analyzes and compares two key methods in this area: generative adversarial network with conditional latent semantic analysis (GAN-CLS) and ultra-long transformer network (XLNet). The main components of GAN-CLS, including the generator, discriminator, and text encoder, are discussed in the context of their functional tasks—generating images from text inputs, assessing the realism of generated images, and converting text descriptions into latent spaces, respectively. A detailed comparative analysis of the performance of GAN-CLS and XLNet, the latter of which is widely used in the organic light-emitting diode (OEL) field, is carried out. The purpose of the study is to determine the effectiveness of each method in different scenarios and then provide valuable recommendations for selecting the best method for generating images from text descriptions, taking into account specific tasks and resources. Ultimately, our paper aims to be a valuable research resource by providing scientific guidance for NLP and CV experts.

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A Comparative Study of Generative Adversarial Networks for Text-to-Image Synthesis

Cited by 25 publications

References 11 publications

Plant Disease Detection using Image Processing

Plant Disease Detection using Image Processing

A Semantic Tree-Based Fast-Moving Object Trajectory Tracking Algorithm for Table Tennis

Generating images using generative adversarial networks based on text descriptions

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