CMX(Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers)是一种利用Transformer模型实现跨模态融合的方法,旨在提高RGB-X(其中X代表其他模态数据,如深度图、红外图像等)语义分割任务的性能。CMX通过融合来自不同模态的信息,使模型能够更全面地理解场景,从而提升分割的准确性和鲁棒性。 2. 阐述cross-...
论文地址:CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers 代码地址:https://github.com/huaaaliu/RGBX_Semantic_Segmentation 本文贡献: 提出了CMX,一种基于vison-transformer的跨模态融合框架,用于RGB-X语义分割(X为RGB的互补模态); 设计了跨模态特征校正模块(CM-FRM),通过结合其他模态...
CMX的主要方框架如下图所示,使用两个并行主干从RGB和X模态输入中提取特征,中间输入 CM-FRM (cross-modal feature rectification module)进行特征修正,修正后的特征继续传入下一层。此外,同一层的特征还被输入FFM(feature fusion module)融合。下面将仔细介绍 CM-FRM 和 FFM。 CM-FRM: cross-modal feature rectificat...
In order to better combine the two modalities, we propose a novel Cross-Modal Transformer for human action recognition鈥擟MF-Transformer, which effectively fuses two different modalities. In spatio-temporal modality, video frames are used as inputs and directional attention is used in the ...
Input fusion:如下图a所示,将RGB和D数据拼接在一起,使用一个网络提取特征。 Feature fusion:如下图b所示,将分别用两个网络提取RGB和D的特征,然后在网络中间进行特征交互融合。 作者提出的CMX,特点为:comprehensive interactions are considered, including channel and spatial-wise cross-modal feature rectification fr...
To this end, we exquisitely design a cross-modal fusion and progressive decoding network (termed CPNet) to achieve RGB-D SOD tasks. The designed network structure only includes three indispensable parts: feature encoding, feature fusion and feature decoding. Specifically, in the feature encoding ...
In the field of vision-based robot grasping, effectively leveraging RGB and depth information to accurately determine the position and pose of a target is a critical issue. To address this challenge, we proposed a tri-stream cross-modal fusion architecture for 2-DoF visual grasp detection. This...
RGB-D salient object detection based on multimodal feature information fusion The key to RGB-D salient object detection is the effective fusion of the different modal features of RGB and depth maps. This study proposes an RGB-D salie... L Meng,M Yuan,X Shi,... 被引量: 0发表: 2023年...
Cross-modal Fusion 首先要将S'的维度降低,做平均池化操作获得\hat{S} \in \mathbb{R}^{p \times d} ,得到clip-level 跨模态融合特征M的过程为:(\hat{V} ,\hat{S}均靠广播为\mathbb{R}^{p \times n \times d }) M = \sigma(FC(\hat{V} \odot \hat{S})) \in \mathbb{R}^{n \...
ABSTRACT:之前的方法直接使用预训练的特征编码器提取外观特征和运动特征(feature concatenation或score-level fusion)。而特征编码器提取是针对于动作分类任务训练的,并不适用于WS-TAL任务,会带来冗余信息和次优化。因此需要对特征重新校准。 提出了CO2-Net,包含两个完全相同的跨模态共识模型(cross-modal consensus modules...