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...
Then, the gated cross-attention feature fusion module (GC-FFM) fuses the expanded modal features to achieve cross-modal global inference by the gated cross-attention mechanism. Utilizing the above two modules in four stages of the network, our framework can learn multi-modal and multi-level ...
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...
To address this challenge, we proposed a tri-stream cross-modal fusion architecture for 2-DoF visual grasp detection. This architecture facilitates the interaction of RGB and depth bilateral information and was designed to efficiently aggregate multiscale information. Our novel modal interacti...
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition 下载积分:199 内容提示: 文档格式:PDF | 页数:15 | 浏览次数:3 | 上传日期:2024-11-11 01:18:20 | 文档星级: 阅读了该文档的用户还阅读了这些文档 8 p. Tensegrity Robot Proprioceptive State ...
In this paper, we propose a cross-modal attention fusion network for RGB-D semantic segmentation. Specifically, we adopt a coordinate attention feature interaction module (CA-FIM) to aggregate RGB and depth features at the spatial and channel levels through the coordinate attention mechanism. Then,...
This method comprises the Image-only module with an integrated multi-view block, the EMR-only module, and the Cross-modal Attention Fusion (CMAF) module. These modules cooperate to extract comprehensive features that subsequently generate predictions for PE. We conducted experiments using the publicly...
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...
1、研究动机 当前的语义分割主要利用RGB图像,加入多源信息作为辅助(depth, Thermal等)可以有效提高语义分割的准确率,即融合多模态信息可以有效提高准确率。当前方法主要包括两种: 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
文献阅读:Multi-Modal and Cross-Scale Feature Fusion Network for Vehicle Detection with Transformers Brickman 我叫继林2 人赞同了该文章 目录 收起 1.研究目的 2模型分析 跨尺度特征融合 3D特征融合模块 查询初始化(Query Initialization) 预测头(Prediction Heads) 最终输出: 实验结果: 时间: 2023年 期刊名...