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-...
A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities. extit{Methods}: We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data ...
然后对象查询的位置嵌入 Γq 可以通过以下方式生成: 其中Apc 和 Aim 分别是投影在 BEV 平面和图像平面上的点集。位置嵌入 Гq 进一步与查询内容嵌入相加,以生成初始位置引导查询 Q0。 3.3.解码器和损失 对于解码器,我们遵循 DETR [46] 中的原始 Transformer 解码器并使用 L 个解码器层。对于每个解码器层,位置...
它首先使用CLIP对文本输入进行编码，然后使用一个映射模块根据文本嵌入调整原始图像的风格代码。SegmentationG...
【ARXIV2203】CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers 高峰OUC 中国海洋大学,计算机学院教师7 人赞同了该文章 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...
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); ...
For applications like robotics, human–computer interaction, autonomous driving, and 3D reconstruction, semantic segmentation is a crucial problem [1], [2], [3], [4]. With the rapid growth of deep learning, semantic segmentation based on RGB has improved in accuracy and speed in recent years...
image-segmentationcross-modalmattingmultimodalimage-matting UpdatedApr 17, 2023 haihuangcode/CMG Star181 The official implementation of Achieving Cross Modal Generalization with Multimodal Unified Representation (NeurIPS '23) pretrained-modelscross-modalmultimodalcross-modal-generalization ...
【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...
We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expression in the image. Existing works in this area treat the language expression and the input image separately in their ...