Cross-modality feature transitionCross-modality feature fusionFeature learningRecent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using deep convolutional neural networks. However, ...
Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery - rubbish-qi/CMAFF
During the bidirectional feature fusion process, the spectral information contained in each modality is learned. Additionally, we design Residual Multiplicative Connections (RMC) to update the fused features at each layer. At the decoding stage, we utilize a Feature Pyramid Aggregation Network (FPN) ...
在FILR 数据集上与其他方法比较的实验结果 在VEDAI 数据集上的实验结果 论文信息 Cross-Modality Fusion Transformer for Multispectral Object Detection arxiv.org/pdf/2111.0027 编辑于 2021-11-15 06:22 深度学习(Deep Learning) Transformer 目标检测
How to design an effective cross-modality fusion mechanism for maximum performance gain?(模态融合) Main innovations 首个将Transformer结构应用到多光谱目标检测的工作 提出一种简单有效的模态融合方法CFT 在VEDAI,LLVIP和FLIR数据集中取得了SOTA How
Cross-modal information retrieval (CMIR) enables users to search for semantically relevant data of various modalities from a given query of one modality. The predominant challenge is to alleviate the “heterogeneous gap” between different modalities. Fo
In the feature fusion part, we design a cross-modal attention fusion module, which can leverage the attention mechanism to fuse multi-modality and multi-level features. In the feature decoding part, we design a progressive decoder to gradually fuse low-level features and filter noise information ...
The main challenge of Pedestrian cm-ReID is that the modality gap between visible and infrared images reduces the recognition effect. To reduce the gap, we propose a novel Pedestrian cm-ReID model called Global–Local Specific Feature Fusion (GLSFF) to integrate the person features extracted by ...
Feature fusion 在获得每一层的特征映射后,我们构建了一个两阶段的特征融合模块(feature Fusion Module, FFM)来增强信息交互,并将两种模式的特征合并成一个单一的特征映射。如上图所示,在阶段1,两个分支仍然保持,并设计了交叉注意机制,在两个分支之间进行全局信息交换。然后,将这两个分支的输出连接起来。在第二阶段...
RoI-wise feature fusion 具体包括: 先将3D proposal的点云转换到标准坐标系,再通过几个MLP编码3D local point feature; 将每个点到传感器的距离作为损失的深度信息的补充; 对于2D proposal,直接从multi-scale feature的最后一层提取image feature,对再通过FC layer将其编码到与3D local point feature相同维度,作为2D...