主要思路和创新点 其实就是在每个连接环节进行了创新,一共提出了两个模块,一个是特征对齐模块(FAM: Feature Alignment Module),一个是特征选取模块(FSM: Feature Selection Module)。作者称这样的结构可以帮助模型获得更多的位置信息。 下面分别说一下两个模块,首先是 FSM。在传统 FPN 中,对于下层特征图,仅进行一...
A key component of our approach is the integration of the Feature Alignment Module (FAM), which is designed to address the complexities of underwater object recognition by enabling the model to selectively emphasize essential features. It combines multi-level features fro...
此外，FaPN还可以通过与轻量级自下而上的主干配对，轻松扩展到实时语义分割。具体方法包括FAM（Feature Alignment Module）和FSM（Feature Selection Module）。FAM类似于SFNet中的对齐操作，但使用3x3可变形卷积进行对齐。FSM是纯粹的通道注意力，但作者解释了二者之间的区别。实验充分表明了FaPN的有效性，并...
prediction. Comprised of a feature alignment module (FAM) and a feature selection module (FSM), FaPN addresses the issue of feature alignment in the originalFPN, leading to substaintial improvements on various dense prediction tasks, such as object detection, semantic, instance, panoptic segmentation...
To address the challenges, we propose a two-stream temporal feature aggregation method based on clustering, incorporating a temporal augmentation module (TAM) and a feature aggregation module (FAM). The TAM adeptly integrates three consecutive grayscale frames into the original RGB frame through ...
To solve the above challenge, we propose a concise, practical, and efficient architecture for appearance and motion feature alignment, dubbed hierarchical feature alignment network (HFAN). Specifically, the key merits in HFAN are the sequential Feature AlignMent (FAM) module and the Feature ...
Finally, the feature alignment module (FAM) utilizes convolutions to obtain a learnable offset map and aligns feature maps with different resolutions, helping to recover details and refine feature representations. Extensive experiments conducted on the ISPRS Vaihingen, Potsdam, and LoveDA ...
FAM (feature alignment module) is feature alignment module, which is explained in Section 3.2.1, and the experimental results show that each component in the proposed feature alignment module had a positive impact on the model performance. Table 9. Quantitative results of ablation experiments with ...
FAM (feature alignment module) is feature alignment module, which is explained in Section 3.2.1, and the experimental results show that each component in the proposed feature alignment module had a positive impact on the model performance. Table 9. Quantitative results of ablation experiments with ...
distance to calculate the regression loss, offering a different approach to bounding box representation. S2A-Net [29] introduces a feature alignment module and a direction regression module into the detection head, which facilitate the generation of high-quality anchors and improve the balance between...