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...
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...
【ARXIV2203】CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers 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...
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 ...
RGB-D semantic segmentationProposes CSCA, a plug-and-play module to improve the performance of multimodal crowd counting and image segmentation tasks.Introduces computationally efficient spatial-level attention and channel-level recalibration.Achieves new state-of-the-art methods in RGB-Depth, RGB-...
Existing RGB-depth semantic segmentation methods primarily rely on symmetric two-stream Convolutional Neural Networks (CNNs) to extract RGB and spatial features separately. However, these architectures have limitations in incorporating spatial features and efficiently fusing RGB and depth information. In thi...
With the rapid growth of deep learning, semantic segmentation based on RGB has improved in accuracy and speed in recent years [5], [6], [7], [8], [9], [10], [11], [12]. However, for a variety of task-driven robots or 3D scene understanding, RGB semantic segmentation cannot ...
【ARXIV2203】CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers 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:...
Semantic segmentation is one of the most important tasks in the field of computer vision. It is the main step towards scene understanding. With the advent of RGB-Depth sensors, such as Microsoft Kinect, nowadays RGB-Depth images are easily available. This has changed the landscape of some ...
RGB-thermal segmentationMultimodal fusionThe Visual Computer - Semantic segmentation is a basic task in computer vision, which is widely used in various fields such as autonomous driving, detection, augmented reality and so on. Recent...doi:10.1007/s00371-022-02559-2Fu, Yanping...