文献阅读:ICAFusion: Iterative cross-attention guided feature fusion for multispectral object detection Brickman 我叫继林4 人赞同了该文章 目录 收起 1.研究目的 2.模型结构(创新点) 3.实验分析 时间: 2024年 期刊名称: Pattern Recognition 论文地址: doi.org/10.1016/j.patco 中科院分区: 1区 作者: ...
Cross-attention Norm2 加快训练,但为什么要在这里 FFM Concatention 最后将俩个特征逐元素加后,输入到了SFC中 SFC是什么? Separate Fully Connected Layer (SFC) is used for the feature mapping in the Encoding and Fusion stage. "独立全连接层"这个术语表明可能存在多个全连接层的实例,并且它们被保持...
A novel dual cross-attention feature fusion method is proposed for multispectral object detection, which simultaneously aggregates complementary information from RGB and thermal images.An iterative learning strategy is tailored for efficient multispectral feature fusion, which further improves the model perform...
In this paper, we propose a novel feature fusion framework of dual cross-attention transformers to model global feature interaction and capture complementary information across modalities simultaneously. In addition, we introdece an iterative interaction mechanism into dual cross-attention transformers, ...
graphvisual-question-answeringfeature-fusiongraph-attention-networkgraph-matcing-networkcross-modality-vqa UpdatedFeb 16, 2023 Python A selection of RGB-T object tracking papers and their performance on various benchmarks. deep-learningfeature-fusionrgb-thermalrgb-t-trackingrgb-thermal-trackingrgbtrgb-t...
In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considerin...
Although such attention-based meth- ods present nonlinear approaches for feature fusion, they still suffer from the following shortcomings: 1. Limited scenarios: SKNet and ResNeSt only focus on the soft feature selection in the same layer, whereas the cross-layer fusion in skip connections has not...
Feature Fusion Attention Network(FFANet)是一种用于单图像去雾(Single Image Dehazing)的深度学习方法。以下是对FFANet的详细解答: 1. FFANet的基本原理 FFANet是一种端到端的特征融合注意力网络,旨在直接恢复无雾图像。它通过结合特征融合和注意力机制,有效处理图像中的雾霾问题。FFANet的核心思想在于,通过特征注意...
cross-attention unified attention Q-former (引入可学习的query向量)先对齐长度(例如插值法)，再采用第...
Feature Fusion Attention 首先,将Group Architecture在通道方向上输出的所有特征图连接起来。此外,通过乘以通道特征注意力机制获得的自适应学习权重来融合特征。由此,可以保留低级信息并将其传递到更深的层次,由于权重机制,让FFA-Net更加关注有效信息,例如浓雾区域,高频纹理和色彩逼真度。