Specifically, we design an innovative multi-scale feature extraction and fusion module to comprehensively capture the details and contextual information of the target, enabling us to obtain stronger semantic and visual edge information. The multi-modal feature fusion module uses enhanced channel and ...
In this paper, we propose a novel Multi-scale Feature Extraction and Fusion method (MFEF) for online knowledge distillation, which comprises three key components: Multi-scale Feature Extraction, Dual-attention and Feature Fusion, towards generating more informative feature maps for distillation. The ...
摘要: feature extraction use multi-scale strategy and channel spatial fusion strategy.Introduce a self-attention interaction module to facilitate fast convergence.Our proposed method achieved the best performance compared to other methods.关键词: KeywordsSingle-object trackingRGB-T trackingFeature fusion ...
We introduce an end-to-end framework named the multi-level feature fusion neural network (MFNet) for the tasks of classification and part segmentation, which effectively fuse features of different scales and levels. Furthermore, we design a network MFNet-S for semantic segmentation tasks and conduc...
Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process To address its froth image segmentation problem, this article proposes a multiscale feature extraction and fusion network (MsFEFNet) to overcome the multi... Y Wang,S ...
Therefore, it is considered that the problem can be solved by designing a multi-scale feature extraction module in terms of both feature extraction and feature fusion, thereby improving the quality of the multi-scale feature representation and thus optimising the effectiveness of the model in ...
technology [4], MSA is gradually becoming an important bridge between human emotion AI security and machine intelligence [5,6]. However, this field also faces many challenges, including the problems of feature extraction, feature fusion, and the handling of different modal contributions [7,8,9]...
This paper addresses the challenges of feature extraction and real-time matching in heterogeneous remote sensing images caused by the differing imaging principles of various sensors. We propose a remote sensing image matching network based on Multiscale feature extraction. The network establishes a matchi...
The key differences between previous MCNN variants with CA-MCNN are concentrated on multi-scale extraction method and multi-scale feature fusion mechanism. We propose a novel signal multi-scale extraction method: Pooling Layers, which can focus on the impact characteristics of the fault signal while...
The experimental results show that the model has achieved average accuracy rates of 73.32% and 97.40% on the Fer 2013 and CK+ datasets. Keywords: computer vision, deep learning, facial expression recognition, feature extraction, multi-scale feature fusion, attention mechanism References [1...