Subsequently, a multi-receptive field fusion-based network (MRFFN) is developed to learn the hierarchical features and synthesize the respective prediction scores to form the final recognition result. As a result, the proposed method is capable of exhibiting an outstanding performance of 99.75% when...
After feature fusion, the channel count of the receptive field features is reduced. Up-sampling is performed using bilinear interpolation. The red feature maps will be visualized and discussed in Section 3.3. 2.5. Global Feature Refinement Module To refine detailed information and expand the ...
Expansive Receptive Field and Local Feature Extraction Network: Advancing Multiscale Feature Fusion for Breast Fibroadenoma Segmentation in Sonography 来自 Springer 喜欢 0 阅读量: 14 作者:Yongxin Guo,Yufeng Zhou 摘要: Fibroadenoma is a common benign breast disease that affects women of all ages. Early...
In response to the second question, we propose a Multi-Receptive-field Fusion Network (MRFNet) for multi-food recognition, which captures unique fine-grained features of Chinese food images using the multi-receptive-field pyramid network, fuses feature information from different receptive fields ...
Aiming at the problem of low detection accuracy of general object detection algorithm in small target detection, a small object detection algorithm S-RetinaNet based on multi-scale receptive field fusion is proposed. The algorithm uses residual neural network (ResNet) to...
The fusion network is an integrated encoder-decoder modal with a multi-receptive-field attention mechanism that is implemented via hybrid dilated convolution (HDC) and a series of convolution layers to form an unsupervised framework. Specifically, the multi-receptive-field attention mechanism aims to ...
In this study, we propose a novel re-parameterized large kernel C3 module, which enables the model to obtain a larger effective receptive field and improve the ability of feature extraction under complex texture interference. Moreover, we construct a feature fusion structure with a multi-path ...
Local receptive fieldMulti-modalRepresentation learningLearning rich representations efficiently plays an important role in the multi-modal recognition task, which is crucial to achieving high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local ...
Expansive Receptive Field and Local Feature Extraction Network: Advancing Multiscale Feature Fusion for Breast Fibroadenoma Segmentation in Sonographydoi:10.1007/s10278-024-01142-6Breast fibroadenomasSonographyComputer-aided diagnosisMedical image segmentation...
We also introduce a Multi-Information Fusion Feature Perception module (MIF) and an Adaptive Receptive Field Selection module (ARFS), which are integrated into the network. Ultimately, we perform thorough comparative experiments on the R2C7K, COD-Water, and COD-Jungle datasets, showcasing superior...