A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic ...
Multiscale feature fusion (MSFF) module Compared with the existing methods, we add a MSFF module to fuse the output of all the layers in the block after the last layer. Let xl be the output of the last layer in the block: $$x_{l} = H(x_{l - 1} ) \otimes H(x_{l - 2...
Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation 2025, AlgorithmsZongwang Han is a Ph.D. Student at the School of Mechanical Engineering, University of Shanghai for Science and Technology. His current research interests include computer vision, human skeleton action recog...
Finally, to effectively utilize the discriminative features of different modalities, we propose an Adaptive Feature Fusion module. This module adaptively fuses appearance and motion features based on their respective feature weights. Extensive experimental results demonstrate that our proposed method outperform...
Then, the feature map with the input sequence after convolution, the position vector, and the classification vector were combined and input into the transformer structure as a whole. The local feature information was captured through the ViT module with sequence information combination to maximize the...
Luo H, Deng G, Hu R et al (2024) CircMAN: multi-channel attention networks based on feature fusion for circRNA-binding protein site prediction. In: 20th International Symposium on Bioinformatics Research and Applications (ISBRA), pp 169–181. https://doi.org/10.1007/978-981-97-5128-0_14...
To solve this problem, we propose a novel multiscale feature fusion network (MSFA-Net). MSFA-Net can extract feature information at different scales through a multiscale feature fusion structure (MSF) in the network and then calibrate and restore the extracted information to achieve the purpose ...
2.3. Feature Fusion Module Design In this paper, two feature fusion modules are designed for high-level and low-level feature fusion, and the fusion effect is compared in our experiments. Concat and eltsum are two common methods of feature fusion. Concat operation is channel concatenation of tw...
Fusion-attention mechanism From the first two modules, we obtain the global feature representation \(\:{Z}=[{{z}}_{1},{{z}}_{2},\ldots,{{z}}_{{n}}]\) embedded with dependency relations and the multi-level local feature representation \(\:{F}=[{{f}}_{1},{{f}}_{2},\l...
Significant feature suppression and cross-feature fusion networks for fine-grained visual classification ArticleOpen access14 October 2024 Introduction Recently, the amount of available data has considerably increased owing to the developments of Internet of Things, technological devices, and computational mach...