本文提出一种Cross-Modality Fusion Transformer(CFT)模块,通过Transformer的能力充分挖掘全局上下文信息。Attention的注意力机制可以同时对模态内和模态间进行特征融合,并提取可见光和红外之间的潜在联系。 模型分析(创新点) 很清晰了,不用多讲了,主要是本文是首次将Transformer运用到多光谱融合目标检测上。实验分析...
论文链接:Cross-Modality Fusion Transformer for Multispectral Object Detection 论文代码:https://github.com/DocF/multispectral-object-detection Motivation 以往CNNs的工作,没有对长距离和全局的信息进行建模。本文提出一种Cross-Modality Fusion Transformer(CFT)模块,通过Transformer的能力充分挖掘全局上下文信息。Attentio...
Firstly, we introduce a cross modality fusion network. The cross modality fusion network is primarily an attention mechanism network. By employing this attention mechanism network to fuse modal information, the network can determine the probability of similarity between the input query image and 3D ...
More importantly, by leveraging the self attention of the transformer, the network can naturally carry out simultaneous intra-modality and inter-modality fusion, and robustly capture the latent interactions between RGB and Thermal domains, thereby significantly improving the performance of multispectral ...
In the feature fusion part, we design a cross-modal attention fusion module, which can leverage the attention mechanism to fuse multi-modality and multi-level features. In the feature decoding part, we design a progressive decoder to gradually fuse low-level features and filter noise information ...
Official Repo of NeurIPS '21: "Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection" - johnwlambert/tbv
实验结果 在三个数据集上 CFT 结构帮助提升的精度 在FILR 数据集上与其他方法比较的实验结果 在VEDAI 数据集上的实验结果 论文信息 Cross-Modality Fusion Transformer for Multispectral Object Detection
In this paper, a transformer-based crossmodality fusion with the EmbraceNet architecture is employed to estimate the emotion. The proposed multimodal network architecture can achieve up to 65% accuracy, which significantly surpasses any of the unimodal models. We provide multiple evaluation techniques ...
部署了一个特征融合模块(feature fusion module, FFM),使用交叉注意力机制构建,在全局上增强两种模态的特征; 一、Motivation 语义分割是计算机视觉中的一项重要任务,其目的是将图像输入转换为其潜在的语义区域,并为许多现实世界的工作所使用。今年来,基于像素级的RGB图像语义分割得到了越来越多的关注,并在分割精度上取...
Cross-modality fusing complementary information from different modalities effectively improves object detection performance, making it more useful and robust for a wider range of applications. Existing fusion strategies combine different types of images or merge different backbone features through elaborated ...