In this paper, we explore the generation of face images conditioned on a textual description, as well as the capabilities of the models in editing a machine-generated image on the basis of additional text prompts. We leverage open source state-of-the...
In today’s digital age, ensuring the appropriateness of content for children is crucial for their cognitive and emotional development. The rise of automated text generation technologies, such as Large Language Models like LLaMA, Mistral, and Ze...
Diffusion models have achieved tremendous success in modeling continuous data modalities, such as images, audio, and video, yet their application in discrete data domains (e.g., natural language) has been limited. Existing methods primarily represent...
Large language models (LLMs) are tools that help us in a variety of activities, from creating well-structured texts to quickly consulting information. But as these new technologies are so easily accessible, many people use them for their own benefit...
Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the cont...
Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task. Many methods based on statistics or machine learning algorithms h...
The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filter...
The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially betwe...
Because interior design is subject to inefficiency, more creativity is imperative. Due to the development of artificial intelligence diffusion models, the utilization of text descriptions for the generation of creative designs has become a novel meth...
This paper presents a methodology for rectifying curved text lines, a crucial process in optical character recognition (OCR) and computer vision. Utilizing generalized additive models (GAMs), the proposed method accurately estimates text curvature an...
Deploying text-to-image (T2I) models is challenging due to high computational demands, extensive data needs, and the persistent goal of enhancing generation quality and diversity, particularly on resource-constrained devices. We introduce a lightweig...
The field of argument analysis has become a crucial component in the advancement of natural language processing, which holds the potential to reveal unprecedented insights from complex data and enable more efficient, cost-effective solutions for enha...
Training fall detection systems is challenging due to the scarcity of real-world fall data, particularly from elderly individuals. To address this, we explore the potential of Large Language Models (LLMs) for generating synthetic fall data. This stud...
Based on their ability to efficiently and intuitively represent real-world relationships and structures, graph databases are gaining increasing popularity. In this context, this paper proposes an innovative integration of a Large Language Model into...
We study the problem of controllable citation text generation by introducing a new concept to generate citation texts. Citation text generation, as an assistive writing approach, has drawn a number of researchers’ attention. However, current re...
The world is currently facing the issue of text authenticity in different areas. The implications of generated text can raise concerns about manipulation. When a photo of a celebrity is posted alongside an impactful message, it can generate outrage,...
Machine-generated content reshapes the landscape of digital information; hence, ensuring the authenticity of texts within digital libraries has become a paramount concern. This work introduces a corpus of approximately 60 k Romanian documents, includ...
The extensive use of large language models (LLMs), particularly in the finance sector, raises concerns about the authenticity and reliability of generated text. Developing a robust method for distinguishing between human-written and AI-generated fina...
Recent advances in vision-language models such as BLIP-2 have made AI-generated image descriptions increasingly fluent and difficult to distinguish from human-authored texts. This paper investigates whether such differences can still be reliably dete...
Efficient and accurate text classification is essential for a wide range of natural language processing applications, including sentiment analysis, spam detection and machine-generated text identification. While recent advancements in transformer-bas...
Current AI detection systems often struggle to distinguish between Arabic human-written text (HWT) and AI-generated text (AIGT) due to the small marks present above and below the Arabic text called diacritics. This study introduces robust Arabic text...
Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold...
With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity prob...
A major hurdle in the development of natural language processing (NLP) methods for Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns prevent the distribution of EHRs, and the annotation of data is known to be...
For the process of covert transmission of text information, in addition to the need to ensure the quality of the text at the same time, it is also necessary to make the text content match the current context. However, the existing text steganography...
Text-to-music generation integrates natural language processing and music generation, enabling artificial intelligence (AI) to compose music from textual descriptions. While AI-enabled music generation has advanced, challenges in aligning text with m...
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s diss...
The task of converting text input into video content is becoming an important topic for synthetic media generation. Several methods have been proposed with some of them reaching close-to-natural performances in constrained tasks. In this paper, we ta...
AI text-to-image generated images have revolutionized the design process and its rapid development since 2022. Generating various iterations of perfect renders in few seconds by textually expressing the design concept. This high-potential tool has op...
The binarization of degraded documents represents a crucial preprocessing task for various document analyses, including optical character recognition and historical document analysis. Various convolutional neural network models and generative models...
With the advent of Transformer-based generative AI, there has been a surge in research focused on large-scale generative language models, especially in natural language processing applications. Moreover, these models have demonstrated immense potenti...
Multi-faceted controllable text generation can be viewed as an extension and combination of controllable text generation tasks. It requires the generation of fluent text while controlling multiple different attributes (e.g., negative emotions and env...
Graph-to-text generation (G2T) involves converting structured graph data into natural language text, a task made challenging by the need for encoders to capture the entities and their relationships within the graph effectively. While transformer-base...
In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operate...
Background: Lumbar spinal stenosis (LSS) is a major cause of chronic lower back and leg pain, and is traditionally diagnosed through labor-intensive analysis of magnetic resonance imaging (MRI) scans by radiologists. This study aims to streamline the...
Data imbalance is a frequently occurring problem in classification tasks where the number of samples in one category exceeds the amount in others. Quite often, the minority class data is of great importance representing concepts of interest and is of...
Text-to-music generation aims to automatically produce audio content with semantic consistency and coherent musical structure based on natural language descriptions. However, existing methods still face challenges in terms of style diversity, rhythmi...
The review enumerates the predominant applications of large language models (LLMs) in natural language processing (NLP) tasks, with a particular emphasis on the years 2023 to 2025. A particular emphasis is placed on applications pertaining to informa...
The lack of extensive, varied, and thoroughly annotated datasets impedes the advancement of artificial intelligence (AI) for medical applications, especially colorectal cancer detection. Models trained with limited diversity often display biases, esp...
In recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is not clear yet how they can be realistically integrated into the artistic...
In view of the insufficiency of the text encoder using CLIP and the insufficiency of the interaction between the two towers using CLIP, a CLIP-based video description model RAMSG is proposed, which combines retrieval augmentation with multi-scale sem...
The lack of three-dimensional (3D) display content has posed significant obstacles to the widespread adoption and development of the industry. However, with the advancement of artificial intelligence (AI), Artificial-Intelligence-Generated Content (A...
Older adults often face significant challenges in understanding medical information due to cognitive aging and limited health literacy. Existing simplification models, while effective in general domains, cannot adapt content for elderly users, freque...
Existing abstractive summarization methods only focus on the correlation between the original words and the summary words, ignoring the topics’ influence on the summaries. To this end, an abstract summarization method combining global topic inf...
The current agent-based evolutionary models for animal communication rely on simplified signal representations that differ significantly from natural vocalizations. We propose a novel agent-based evolutionary model based on text-to-audio (TTA) models...
Text-to-speech (TTS) systems based on neural networks have undergone a significant evolution, taking a step forward towards achieving human-like quality and expressiveness, which is crucial for applications such as social media content creation and v...
The power of the relationship between building information modeling (BIM) systems and advanced artificial intelligence models holds considerable weight for users of BIM. This relationship allows the generation, analysis, and deduction of insights fro...
Current language models have achieved remarkable success in NLP tasks. Nonetheless, individual decoding methods face difficulties in realizing the immense potential of these models. The challenge is primarily due to the lack of a decoding framework t...
Topic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional topical subspace, but it could be better. A topic model’s topic...
Large language models (LLMs) pose significant challenges to content authentication, as their sophisticated generation capabilities make distinguishing AI-produced text from human writing increasingly difficult. Current detection methods suffer from l...
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