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...
RGBX_Semantic_Segmentation The official implementation of CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers (IEEE T-ITS 2023): More details can be found in our paper [PDF]. Usage Installation Requirements Python 3.7+ PyTorch 1.7.0 or higher CUDA 10.2 or higher We have...
RGBX_Semantic_Segmentation The official implementation of CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers: More details can be found in our paper [PDF]. README is still not complete... Usage Installation Requirements Python 3.7+ PyTorch 1.7.0 or higher CUDA 10.2 or ...
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-...
This has changed the landscape of some tasks such as semantic segmentation. As the depth images are independent of illumination, the combination of depth and RGB images can improve the quality of semantic labeling. The related research has been divided into two main categories, based on the ...
In summary, the RGB-D semantic segmentation task has two major problems to solve: 1) how to efficiently fuse the multimodal information between RGB images and depth maps, and 2) how to prevent unreliable depth information from interfering with the network. The aforementioned RGB-D semantic segmen...
RGB thermal semantic segmentation facilitates unmanned platforms to perceive and characterize their surrounding environment, which is critical for autonomo... X Guo,W Zhou,T Liu - Knowledge-Based Systems 被引量: 0发表: 2024年 A geometry-aware attention network for semantic segmentation of MLS point...
Multi-class indoor semantic segmentation using deep fully convolutional neural networks on RGB images has been widely used in scene parsing and human-computer interaction. Due to the wide application of depth information sensors, we can get more understanding of geographic location information from the...
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