Uncover the latest and most impactful research in Computer Vision. Explore pioneering discoveries, insightful ideas and new methods from leading researchers in the field.
Medical image segmentation plays a pivotal role in clinical diagnosis assistance, lesion detection, and computer-aided medicine. However, factors such as complex organ boundary morphologies, multi-scale structural variations, and low contrast...
Automated gastrointestinal (GI) image classification remains challenging because of subtle morphological variation, high inter-class similarity, and the computational constraints of clinical edge devices. We present CA-MobileNetV4, a...
Background Diagnosing glaucoma in highly myopic (HM) eyes remains challenging due to myopia-induced structural alterations of the optic nerve head (ONH) that confound conventional imaging biomarkers and deep learning models. This study developed and...
Rip currents are a common coastal hazard and the leading cause of beach-related drownings worldwide. However, their dynamic and variable appearance makes them difficult for beachgoers to identify, highlighting the potential of automated rip current...
This study proposes an efficient and accurate student behavior recognition framework for smart classroom environments, addressing challenges such as small object scales, posture similarity, occlusion, dense multi-student scenarios, and the trade-off...
Developmental dysplasia of the hip (DDH) is a critical congenital condition in infants, which is generally early diagnosed by B-mode ultrasound (BUS). Deep learning-based computer-aided diagnosis has shown effectiveness in diagnosing DDH from BUS...
Vision foundation models (VFMs) have demonstrated strong generalization ability across various downstream tasks and scenes. However, their vulnerability to adversarial examples, particularly in safety-critical applications, remains insufficiently...
Image steganography is the practice of concealing data within cover images, thereby enhancing confidentiality and protection of information. Nevertheless, traditional methods frequently have issues with low payload capacity, poor imperceptibility,...
Cassava is an important food crop to millions of people across the globe but its productivity is highly compromised by the leaf diseases like the Cassava Mosaic Disease, Cassava Brown Streak Disease and bacterial blight whose symptoms are subtle and...
Long-range small-UAV detection is difficult because targets occupy few pixels and are easily confused with background clutter. Lightweight detectors face a prediction-head capacity conflict: classification requires additional discriminative...
Human pose estimation plays a fundamental role in understanding human behaviors and interactions in complex scenes. Among existing approaches, High-Resolution Networks (HRNet) and their variants have demonstrated strong performance in dense...
Railway foreign object intrusion detection is essential for intelligent railway monitoring systems. However, accurate real-time detection remains challenging due to small and distant targets, complex backgrounds, scale variations, adverse imaging...
Facial Action Units (AUs) have recently been used in dementia detection, pain detection, talking head generation, and even facial reconstruction tasks. The success of each of these applications is at least partially due to the performance of the...
With the rapid growth of multimodal content on social media, multimodal aspect-based sentiment analysis (MABSA) has become important for fine-grained opinion understanding. Existing methods have attempted to incorporate visual information to enhance...
Detecting and tracking vehicles ahead during driving is a critical technology for ensuring driving safety and enhancing the driving experience. However, traditional detection and tracking models often suffer from high computational intensity and...
In this work, we introduce Segmentation to Human–Object Interaction (Seg2HOI), a novel framework that integrates segmentation-based vision foundation models with the human–object interaction (HOI) task, distinguished from traditional detection-based...
Accurate and efficient detection of saffron stigmas is crucial for automation in agricultural applications, particularly for yield estimation and harvesting optimization. In this paper, we propose a lightweight and highly effective detection...
To address the challenges of intrinsically weak feature representations and drastic scale variations of tiny targets in UAV-captured aerial traffic scenes, as well as the prohibitive parameter and computational overheads of existing detection models...
Few-shot semantic segmentation (FSS) aims to enhance the generalization capability of segmentation models to unseen categories by leveraging only a limited number of labeled reference samples. However, traditional FSS methods often struggle with...
This work presents an automated, field-implemented framework that links an anomaly detector (PatchCore with a ResNet50 backbone) to a Vision-Language Model (VLM) to produce concise, inspection-ready reports from imagery. The approach is demonstrated...
Multimedia Tools and Applications(MTAP) publishes high-quality original research articles, surveys, technical reports, and case studies that advance...
The journal is an interdisciplinary journal presenting the theory and practice of signal, image and video processing. It aims at: Disseminating high...
The International Journal of Computer Vision(IJCV) provides a forum for the dissemination of new research results in the rapidly growing field of...
