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...
这说明CFT模块在处理过程中进行了特征的提取与融合,去除了原始特征中的噪声或不重要的信息,仅保留了与检测任务相关的关键特征。 展示了原始特征与经过Cross-Modality Fusion Transformer (CFT) 模块处理后的特征的可视化对比,旨在说明CFT模块在特征提取和信息融合中的作用。发布于 2024-09-03 15:33・IP 属地北京 内...
To fully exploit the different modalities, we present a simple yet effective cross-modality feature fusion approach, named Cross-Modality Fusion Transformer (CFT) in this paper. Unlike prior CNNs-based works, guided by the transformer scheme, our network learns long-range dependencies and integrates...
Learning cross-modality fusion is a crucial step of VideoQA. How to ensure that the fused representation well preserves the valuable temporal characteristic of videos is the key research question of robust VideoQA. In this work, to prevent the model from leveraging the spurious correlation between...
Natural language processing and vision tasks have recently seen large improvements through the rise of Transformer architectures. The high-performing large language models (LLMs) benefit from large textual datasets that are numerously available online. However, action and bidirectional action-language tasks...
et al. Spatial transformer networks. In Advances in Neural Information Processing Systems vol. 28 (2015). 30. Jiang, N. et al. Anti-uav: A large multi-modal benchmark for uav tracking. arXiv preprint arXiv:2101.08466 (2021). 31. Cao, X. et al. Deep learning based ...
Through experiments, we demonstrate that a simple stack of transformer encoder layers can substitute complex fusion modules with better-performing alternatives. We validate the efficacy of our suggested model and exhibit SOTA performance using the benchmark dataset RSVGD. 展开 ...
In the cross-attention module, we stack the representations of image regions and sentence words and then pass them into another Transformer unit fol- lowed by a 1d-CNN [16] and a pooling operation to fuse both inter-modality and intra-modality information. ...
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
作者选用的Baseline是RoiTransformer的架构,并在这个基础上修改了网络架构并使其适应跨模态的输入和输出。CMDet由三条分支组成,RGB infrared fusion中。Fusion是两个模态的融合分支。两个模态选用的Backbone分别是对应的ResNet 50。然后将对应两个模态的Feature map,concat后利用1*1卷积进行维数约减的操作。由于Baseline...