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...
With the increasing demand for enhanced security and surveillance, the integration of multimodal AI has shown significant promise. We developed and fine-tuned a transformer-based model, the Large Language and Vision Assistant–OneVision, tailore...
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...
Ensuring navigational safety is one of the most critical challenges in autonomous maritime navigation research, requiring accurate real-time assessment of collision risks and prompt navigational decisions based on such assessments. Traditional rule-b...
Vision–Language Models (VLMs) have emerged as powerful tools for image understanding tasks, yet their practical deployment remains hindered by significant architectural heterogeneity across model families. This paper introduces UVLM (Unified Vi...
Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-making aspect of autonomous driving, behavior decision establishes short-term driving behavior strategies by evaluating road structures, adhering to traffic rul...
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...
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-...
Very low-bit quantization of a multimodal large language model’s vision encoder can impair text recognition and chart understanding. We present projected-feature scale calibration (PSC), an image-only post-training method for 2-bit vision encod...
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...
Current vision–language models (VLMs) are well-adapted for general visual understanding tasks. However, they perform inadequately when handling complex visual tasks related to human poses and actions due to the lack of specialized vision–...
Accurate brain-tumour segmentation and longitudinal magnetic resonance imaging (MRI) registration require representations capable of capturing both local tissue characteristics and long-range anatomical dependencies. Inspired by the hierarchical loca...
Visual large language models (VLLMs) are discussed as potential tools for assisting radiologists in image interpretation, yet their clinical value remains unclear. This study provides a systematic and comprehensive comparison of general-purpose and b...
E-commerce search engines often fall short in delivering accurate and fair results due to their reliance on keyword matching and bias towards larger companies. This paper introduces a novel search engine architecture that integrates advanced AI techn...
The rise of toxic content, particularly in the form of hateful memes, poses a significant challenge to social media platforms. This paper presents an empirical comparative study of unimodal and multimodal architectures for toxic content detection. Ra...
Drone-based smart sensing increasingly relies on Vision–Language Models (VLMs) for real-time scene interpretation, obstacle detection, and autonomous navigation reasoning. Deploying such systems at scale demands not only high perceptual accurac...
Rapidly growing medical imaging volumes have increased radiologist workloads, creating demand for automated tools that support interpretation and reduce reporting delays. Vision-language models (VLMs) can generate clinically relevant captions to acce...
As large language models (LLMs) and vision–language models (VLMs) become increasingly used in robotics area, a crucial question arises: to what extent do these models replicate human-like cognitive processes, particularly within socially intera...
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...
The generalization of safety mechanisms from text-only LLMs to Large Vision–Language Models (LVLMs) remains poorly understood, especially concerning their vulnerability to typographic adversarial prompts. In this paper we ask where, inside a mo...
Disaster-related social media posts combine images and text, but the benefits of large language model (LLM)-derived representations for crisis classification remain uncertain. We evaluate three pretrained vision–language pipelines—CLIP, L...
With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents...
Voice activity detection (VAD) is the process of automatically determining whether a person is speaking and identifying the timing of their speech in an audiovisual data. Traditionally, this task has been tackled by processing either audio signals or...
Concept Bottleneck Models (CBMs) route predictions through a layer of human-understandable concepts, but classical CBMs need a concept label for every training image, a cost that is high in medicine. Vision–language models (VLMs) remove that re...
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...
Deep convolutional neural networks have made substantial progress in automated crop disease diagnosis. However, their practical application remains constrained by limited interpretability and insufficient structured reasoning, as these models largely...
Vision-language models (VLMs) are increasingly evaluated for retinal image interpretation, but end-task discrimination does not establish whether a prediction depends on the optical coherence tomography (OCT) image. We evaluated RetinaVLM, LLaVA-Med,...
Large Multimodal Models are emerging general AI models capable of processing and analyzing diverse data streams, including text, imagery, and sequential data. This paper explores the possibility of exploiting multimodality to develop more interpretab...
This preliminary study covers the construction and application of a Graph-based Retrieval-Augmented Generation (GraphRAG) system integrating a multimodal LLM, Large Language and Vision Assistant (LLaVA) with graph database software (Neo4j) to enhance...
The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigati...
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...
Multimodal large language models (MLLMs) face substantial memory bottlenecks when processing long visual contexts, such as videos and high-resolution images. Existing methods that allocate visual KV cache budgets using cross-modal attention entropy m...
Accurate detection in the early stages of a fire is a crucial prerequisite for the efficient implementation of fire suppression and emergency rescue operations. Its accuracy and timeliness directly affect the control of disaster loss severity. Tradit...
Background: Automated brain-MRI analysis is usually judged by accuracy at a single operating point, yet a model that is accurate on average may still fail without signalling that it is about to fail. Selective prediction addresses this by allowing a...