In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing (RS) image analysis. We particularly emphasize their multi-tasking poten...
Autonomous driving (AD) has emerged as a transformative technology that holds the potential to free humans from the need for manual driving and provide a safer, more comfortable and efficient driving experience. However, most AD systems make decision...
The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Int...
Deploying medical visual question answering (VQA) systems over wireless networks introduces a fundamental challenge: channel-induced image degradation may corrupt the visual representations extracted by large vision-language models (VLMs), leading to...
With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables machines t...
This survey provides a comprehensive guide to Multimodal Large Language Models (MLLMs) with a focus on vision–language tasks, including image captioning, visual question answering, cross-modal retrieval, visual grounding, multi-image reasoning,...
Multimodal large language models (MLLMs) and vision–language models (VLMs) have rapidly entered medicine, demonstrating promising performance in clinical reasoning, radiology report generation, and visual question answering (VQA). However, many...
This paper studies geometric scene formalization in vision-based educational sensing environments, where textual conditions and geometric diagram images jointly constitute heterogeneous perceptual inputs. The goal is to convert multimodal sensed info...
Smart manufacturing demands ever-increasing equipment reliability and continuous availability. Traditional fault diagnosis relies on attached sensors and complex wiring to collect vibration signals. This approach suffers from poor environmental adapt...
Large Vision–Language Models (LVLMs) have achieved strong performance in multimodal understanding and generation. However, they remain prone to hallucination, where generated content deviates from the visual input, reducing output reliability....
Building on advances in promptable segmentation models, this work introduces a framework that integrates Large Vision-Language Model (LVLM) bounding box priors with prototype-based region of interest (ROI) selection to improve zero-shot medical image...
Few-shot object detection (FSOD) in high-resolution remote sensing (RS) imagery remains challenging due to scarce annotations, large intra-class variability, and high visual similarity between categories, which together limit the generalization abili...
Understanding advanced pre-tertiary mathematics, particularly three-dimensional vectors, demands robust spatial reasoning skills that many students find challenging to develop through traditional pedagogical methods. This study proposes and evaluates...
Fake news has caused significant harm and disruption across various sectors of society. With the rapid advancement of the Internet and social media platforms, both academic and industrial communities have shown growing interest in multimodal fake new...
Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimod...
Multimodal aspect-based sentiment analysis (MABSA) predicts the sentiment expressed toward a target aspect by jointly using textual and visual information, supporting fine-grained opinion analysis in product reviews, brand monitoring, and customer fe...
Reliance on standalone accuracy limits credible assessment of fundus-focused large vision–language models (LVLMs), as high scores often stem from linguistic shortcuts rather than real visual reasoning. This work develops the Cognitive Audit Fra...
Accurately assessing urban green perception is crucial for sustainable urban development and human well-being, yet conventional approaches often depend on simplistic objective metrics and non-immersive, screen-based subjective surveys, undermining ec...
Image aesthetic assessment (IAA) has progressed from handcrafted visual features to deep neural models, yet fine-art evaluation remains difficult because aesthetic judgment depends on style, historical context, and formal composition. Large Vision-La...
Urban recreational spaces (URSs) are pivotal for enhancing resident well-being, making the accurate assessment of public perceptions crucial for quality optimization. Compared to traditional surveys, social media data provide a scalable means for mul...
Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. However, existing methods often suffer from poor generalizatio...
Air-ground collaborative systems (AGCS), in which unmanned aerial vehicles survey from above while ground vehicles navigate the terrain below, can achieve capabilities far beyond those of any single platform when they share perception and coordinate...
Large language models (LLMs) and large vision models (LVMs) have driven significant advancements in natural language processing (NLP) and computer vision (CV), establishing a foundation for multimodal large language models (MLLMs) to integrate divers...
With the rapid development of large visual language models (LVLMs) and multimodal large language models (MLLMs), these models have demonstrated strong performance in various multimodal tasks. However, alleviating the generation of hallucinations rema...
Identity verification is important for safety management at industrial access points. Gait recognition provides contactless identity cues as workers pass through a gate, but public datasets rarely combine card-swiping, moving barriers, standard workw...
Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks...
The work presented herein proposes DisasterReliefGPT, a multimodal AI system for automation in the areas of crisis communication and post-disaster assessment. The system integrates three tightly coupled components: a vision module called DisasterOCS...
Large vision language models (LVLMs) are built upon large language models (LLMs) and incorporate non-textual modalities; they can perform various multimodal tasks. Applying LVLMs in remote sensing (RS) visual question answering (VQA) tasks can take a...
This study introduces an interpretable deep learning pipeline for citrus fruit quality classification using the ViT-Base model vit_base_patch16_224 and Microsoft’s Phi-3-mini LLM. The ViT model, fine-tuned on resized 224 × 224 images with...
Foundation models have shown remarkable generalization ability for few-shot learning (FSL). However, their potential for remote sensing scene classification has not been fully explored. Existing methods mainly adapt a single vision–language mod...
Large Vision Language Models (LVLMs) have shown promise in remote sensing applications, yet struggle with “uncommon” objects that lack sufficient public labeled data. This paper presents Enhanced-RSCLIP, a novel dual-prompt architecture t...
The rapid development of large artificial intelligence (AI) models—large language models (LLMs), multimodel large language models (MLLMs) and vision–language models (VLMs)—has enabled instruction-driven visual understanding, where a...
Recent advances in multi-modal large language models have enabled systems to jointly process text, images, and audio. However, these developments have primarily benefited high-resource languages, leaving many low-resource communities underserved. In...
This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background a...
Target ranging is the premise for manipulators to complete agronomic operations such as picking and field management; however, complex environmental backgrounds and changing crop shapes increase the difficulty of obtaining target distance information...
As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal c...
Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLM...
Small-scale and urban indoor farms typically rely on manual observation, which delays stress detection and yields inconsistent crop quality. While large language models (LLMs) and retrieval-augmented generation (RAG) have been explored for agricultur...
Population ageing and chronic disease are increasing demand for rehabilitation, while resources remain limited. This review does not report an implemented end-to-end system; instead, it proposes a modular workflow framework for applying large AI foun...
Children increasingly consume online video content, creating a growing need for scalable approaches to support content moderation workflows. However, directly identifying harmful or policy-violating content, such as violence, sexual content, or self-...
Understanding information extracted from receipts is a critical task for real-world applications such as financial tracking, auditing, and enterprise resource management. In this paper, we introduce ReceiptQA, a novel large-scale dataset designed for...
This paper focuses on improving automated approaches to glaucoma diagnosis, a severe disease that leads to gradually narrowing vision and potentially blindness due to optic nerve damage occurring without the patient’s awareness. Early diagnosis...
Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery...
Remote sighted assistance (RSA) has emerged as a conversational technology aiding people with visual impairments (VI) through real-time video chat communication with sighted agents. We conducted a literature review and interviewed 12 RSA users to und...
Autonomous driving in complex real-world environments requires robust perception, reasoning, and physically feasible planning, which remain challenging for current end-to-end approaches. This paper introduces VLA-MP, a unified vision-language-action...
Reward functions determine what reinforcement learning agents ultimately optimize, yet reward design for complex tasks has traditionally relied on extensive domain expertise and iterative engineering. Recent large language models and vision–lan...
Foundation models (FMs) and vision-language models (VLMs) have emerged as a transformative paradigm in artificial intelligence, reshaping the landscape of medical image analysis and clinical decision support. Building upon large-scale pretraining on...
In recent times, transformer-based deep learning models have risen in prominence in the field of machine learning for a variety of tasks such as computer vision and text generation. Given this increased interest, a historical outlook at the developme...
The rapid scaling of large language model (LLM) training has intensified demand for Graphics Processing Unit (GPU) clusters balancing throughput with energy efficiency. While NVIDIA’s H100 and B200 architectures are increasingly deployed in pro...
Virtual assistants in academic environments often lack comprehensive multimodal integration and specialized role-based architecture. This paper presents NAIA (Nimble Artificial Intelligence Assistant), a robust artificial intelligence framework desig...
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