Gaborformer: A method for depression detection through hybrid acoustic feature extraction and fusion.

iAFP-fLRM: Accurate identification of antifungal peptides via hybrid deep learning architecture and multi-modal feature fusion.

Anatomy-guided prompting with cross-modal self-alignment for whole-body PET-CT breast cancer segmentation.

A data-centric approach to radar-based human action recognition: SVD-based clutter removal and RTM/DTM feature fusion

Advancing automatic speech recognition using feature fusion with self-supervised learning features: A case study on Fearless Steps Apollo corpus

Underwater object detection based on channel expansion and feature fusion

• Introduced DDConv, a dual-path structure designed to simultaneously extract local detail and global context. • Incorporated Dy-CCFM, a dynamic weighting mechanism that adaptively fuses multi-scale semantic and spatial information. • Integrated Minimum Point Distance IoU (MPDIoU) to effectively address regression issues, thereby improving localization accuracy for small defects. In contemporary computer vision, You Only Look Once (YOLO) has become a benchmark for object detection, widely used in domains from intelligent manufacturing-such as industrial quality control and automated inspection-to real-time video surveillance. For example, detecting surface defects on steel products or electronic components in production lines relies on such algorithms to maintain high quality and safety. Despite YOLO’s excellent speed and accuracy in many tasks, it still faces difficulties in certain challenging conditions, notably high dynamic range scenes, complex backgrounds, and the detection of small or subtle objects. These conditions are common in practice-for instance, on shiny metal surfaces with uneven lighting or in busy surveillance scenes-where conventional YOLO models struggle to capture fine details reliably. To overcome these limitations, we propose an improved YOLO-based framework featuring a novel Dynamic Cross-Scale Feature Fusion Module (Dy-CCFM) and a Dual-path Downsampling Convolution Module (DDConv). These modules enhance multi-scale feature representation and preserve detail under extreme lighting and background clutter, which is crucial for monitoring in complex environments. Additionally, we employ the Minimum Point Distance Intersection over Union (MPDIoU) as an optimized loss function for bounding box regression, significantly improving the localization of small objects. Thanks to these innovations, the model achieves a mean Average Precision (mAP) of 75.1 % on the challenging Northeastern University surface defect (NEU-DET) dataset, while the smallest variant is only 1.6M in size. Compared to YOLOv8, our approach improves mAP by 2.1 % while also delivering higher inference speed (FPS), and it surpasses the Detection Transformer (DETR) by 5.0 % mAP. The model further demonstrates excellent generalization on the Google Cloud 10 Defect Detection (GC10-DET) dataset. This enhanced detection algorithm not only improves performance but also offers significant practical value in intelligent manufacturing and automated inspection systems, intelligent video surveillance, and autonomous vehicles, where reliable real-time detection of small defects or targets is critical.

Enhancing feature fusion of U-like networks with dynamic skip connections.

Omnidirectional cameras are a suitable and cost-effective choice for Visual Place Recognition (VPR), as they provide comprehensive information from the scene regardless of the robot orientation. However, vision sensors are vulnerable to environmental appearance changes (e.g., illumination, weather, season or moving objects). While multi-modal sensing approaches can overcome these challenges, they introduce significant cost and system complexity. This paper introduces PDPR (Panoramic-Depth Place Recognition), a novel fusion framework that enhances the robustness of VPR methods by integrating visual data with geometric features derived from monocular depth estimation techniques, while using a single-camera setup. In the ablation study, both early and late fusion strategies are evaluated to optimally combine appearance-based and depth-derived features. The extensive evaluation on challenging, indoor and outdoor datasets demonstrates that PDPR consistently boosts retrieval performance across multiple state-of-the-art VPR models. Furthermore, this improvement is achieved without requiring any fine tuning, allowing our method to function as a pluggable module for pretrained models. Consequently, this work presents a powerful, practical and low-cost solution for robust VPR, with high potential to scale as monocular depth estimation and VPR models continue to improve. The project website can be found at https://marcosalfaro.github.io/projects-PDPR/ . • Monocular depth estimation is used to enhance place recognition. • A thorough evaluation of preprocessing techniques to enhance the depth maps. • Fusion techniques are designed to leverage visual and geometric data. • A model-agnostic approach that improves the performance even with no fine tuning. • A robust method across different scenarios and lighting conditions.

Fusion of deep and manual features for improved generation of large-size, low-resolution functional medical images

Cross-modal fusion of chemical language and physicochemical features enables accurate and interpretable multi-label odor prediction.

Multimodal sparse fusion transformer network with spatio-temporal decoupling for breast tumor classification.

An AUV fault diagnosis method based on composite feature extraction and fusion from multi-sensor sequence chain graphs

Accurate segmentation of optic disc regions in fundus images is essential for the diagnosis and monitoring ocular fundus pathology of ophthalmic diseases such as myopia, glaucoma, and diabetic retinopathy. However, challenges such as irregular disc shapes, edge detail preservation, and limited annotated datasets hinder the effectiveness of existing methods. To handle these problems, we propose FDAU-Net, an efficient, fully convolutional network for optic disc segmentation. FDAU-Net integrates the Flexible Wavelet Convolution Block (FWC Block), which combines the dynamic receptive field adjustment of AKConv with wavelet down-sampling to achieve efficient feature extraction while retaining critical edge information. Additionally, we design the Selective Dual-Attention Fusion Gate (SDA Gate) to optimize feature selection and fusion, leveraging channel and spatial attention mechanisms to substantially enhance segmentation accuracy and robustness. To further improve performance, we introduce MedAugment, an automated data augmentation method tailored for medical images, efficiently boosts data diversity in scenarios with low datasets. Experimental results on the iChallenge and IDRiD datasets demonstrate that FDAU-Net consistently achieves superior segmentation performance across key metrics, highlighting its potential for advancing clinical applications.

A quality prediction method for injection molding products based on multi-stage feature decoupling and fusion

MRFF-DSPP-RI U-Net: Renal tumor segmentation using multiresolution feature fusion model based on enhanced u-net with dilated spatial pyramid pooling

ViTextVQA: A large-scale visual question answering dataset and a novel multimodal feature fusion method for Vietnamese text comprehension in images

Extraction of 10 m shelterbelts in Northeast China using multidimensional phenological characteristics derived from Sentinel-2 images

EfficientNet-based multi-scale feature fusion with hybrid spatial-channel attention for precise liver and tumor segmentation in CT scans

Direction-sensitive vibration feature fusion for wear evolution characterization of water-lubricated bearings