Open-vocabulary semantic segmentation (OVSS) is of critical importance for unmanned aerial vehicle (UAV) imagery, as UAV scenes are highly dynamic and characterized by diverse, unpredictable object categories. Current OVSS approaches mainly rely on t...
Multimodal learning is a promising area in artificial intelligence (AI) that can make the model understand different kinds of data. Existing works are trying to re-train a new model based on pre-trained models that requires much data, computation pow...
To assess the efficiency of vision–language models in detecting and classifying carious and non-carious lesions from intraoral photo imaging. A dataset of 172 annotated images were classified for microcavitation, cavitated lesions, staining, ca...
1 August 2025
In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). Th...
Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large...
Parameter tuning based adapter methods have achieved notable success in vision-language models (VLMs). However, they face challenges in scenarios with insufficient training samples or limited resources. While leveraging image modality caching and ret...
Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in Vision–Language Models (VLMs) have pushed this enthusiasm to new heights. Differing from previous AI app...
Weakly supervised crack segmentation aims to create pixel-level crack masks with minimal human annotation, which often only differentiate between crack and normal no-crack patches. This task is crucial for assessing structural integrity and safety in...
Because the pretraining model is not limited by the scale of data annotation and can learn general semantic information, it performs well in tasks related to natural language processing and computer vision. In recent years, more and more attention ha...
In the diagnosis of lung cancer, imaging findings of lung nodules are essential for benign and malignant classifications. Although numerous studies have investigated the classification of lung nodules, no method has been proposed for obtaining detail...
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...
Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale p...
Background: Vision-language models show promise in medical image interpretation, but their performance in musculoskeletal tumor diagnostics remains underexplored. Objective: To evaluate the diagnostic accuracy of six large language models on orthoped...
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...
Despite advances in complex reasoning, Vision-Language Models (VLMs) remain inadequately benchmarked for safety-critical applications like childcare. To address this gap, we conduct a multilingual (English, French, Polish, Japanese) comparison of VLM...
Purpose: The purpose of this study was to compare multiple deep learning models for estimating age and sex using dental panoramic radiographs and identify the most successful deep learning models for the specified tasks. Methods: The dataset of 437 p...
Marine low clouds have a strong impact on Earth’s system but remain a major source of uncertainty in anthropogenic radiative forcing simulated by general circulation models. This uncertainty arises from incomplete understanding of the many proc...
Recently, prompt learning has emerged as a viable technique for fine-tuning pre-trained vision–language models (VLMs). The use of prompts allows pre-trained VLMs to be quickly adapted to specific downstream tasks, bypassing the necessity to upd...
Medical Visual Question Answering (MedVQA) lies at the intersection of computer vision, natural language processing, and clinical decision-making, aiming to generate accurate responses from medical images paired with complex inquiries. Despite recent...
Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collabo...
This study focuses on Scene Text Recognition (STR), which plays a crucial role in various applications of artificial intelligence such as image retrieval, office automation, and intelligent transportation systems. Currently, pre-trained vision-langua...
Recent vision–language models (VLMs) achieve strong performance across multimodal benchmarks but suffer from high inference costs due to the large number of visual tokens. Prior studies have shown that many image tokens receive consistently low...
Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have prov...
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...
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...
Camera relocalization, a cornerstone capability of modern computer vision, accurately determines a camera’s position and orientation from images and is essential for applications in augmented reality, mixed reality, autonomous driving, delivery...
Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatia...
As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image pro...
Vision–Language Models (VLMs) have become key contributors to the state of the art in contextual emotion recognition, demonstrating a superior ability to understand the relationship between context, facial expressions, and interactions in image...
Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily...
Weakly supervised video anomaly detection aims to identify abnormal events in video sequences without requiring frame-level supervision, which is a challenging task in computer vision. Traditional methods typically rely on low-level visual features w...
The Controller Area Network (CAN) facilitates efficient communication among vehicle components. While it ensures fast and reliable data transmission, its lightweight design makes it susceptible to data manipulation in the absence of security layers....
The confluence of computer vision and natural language processing has yielded powerful vision language models (VLMs) capable of multimodal understanding. We applied state-of-the-art VLMs for quality monitoring of the shoe assembly industry. By levera...
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...
This paper presents the experimental evaluation and analyzes the results of the first edition of the pedestrian attribute recognition (PAR) contest, the international competition which focused on smart visual sensors based on multi-task computer visi...
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...
Zero-shot classification presents a challenge since it necessitates a model to categorize images belonging to classes it has not encountered during its training phase. Previous research in the field of remote sensing (RS) has explored this task by tr...
Thermal comfort in urban commercial spaces significantly impacts both business performance and public well-being. Traditional evaluation methods relying on field surveys and expert assessments are often time-consuming and labor-intensive. This study...
Bridge health diagnosis plays a vital role in ensuring structural safety and extending service life while reducing maintenance costs. Traditional structural health monitoring approaches rely on sensor-based measurements, which are costly, labor-inten...
Building cracks are among the critical building defects, as they can compromise structural integrity, occupant safety and building sustainability. Traditional laborious building inspection methods are cumbersome and erroneous. Computer vision-based c...
Large vision–language models (VLMs) can describe images fluently, yet their anomaly decisions often rely on opaque heuristics and manual thresholds. We present ThinkAnomaly, a rationale-first vision–language framework for industrial anoma...
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...
Autonomous Underwater Vehicles (AUVs) equipped with vision systems face unique challenges in real-time environmental perception due to harsh underwater conditions and computational constraints. This paper presents a novel cloud–edge framework f...
Precise classification of unsound wheat grains is essential for crop yields and food security, yet most existing approaches rely on vision-only models that demand large labeled datasets, which is often impractical in real-world, data-scarce settings....
This article presents CapFlow, an integrated approach to detailed image captioning and hashtag generation. Based on a thorough performance evaluation, the image captioning model utilizes a fine-tuned vision-language model with Low-Rank Adaptation (Lo...
The accurate modelling of video memorability, or the intrinsic properties that render a piece of audiovisual content more likely to be remembered, will facilitate the development of automatic systems that are more efficient in retrieving, classifying...
As oil and gas exploration has deepened, the complexity and risk of well repair operations has increased, and the traditional description methods based on text and charts have limitations in accuracy and efficiency. Therefore, this study proposes a w...
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–...
The integration of vision–language models (VLMs) with robotic systems represents a transformative advancement in autonomous task planning and execution. However, traditional robotic arms relying on pre-programmed instructions exhibit limited ad...
Abnormal phenomena on urban roads, including uneven surfaces, garbage, traffic congestion, floods, fallen trees, fires, and traffic accidents, present significant risks to public safety and infrastructure, necessitating real-time monitoring and early...
of 13