HRTransNet：HR格式驱动的双模态显著性目标检测
HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection
摘要：
高分辨率Transformer（HRFormer）可以保持高分辨率表示并共享全局感受野。该算法对显著目标检测（SOD）具有较好的适应性，输入输出分辨率相同。然而，双模态SOD需要解决两个关键问题。一个问题是双模态融合。另一个问题是HRFormer输出的融合。为了解决第一个问题，一个补充的模态被注入到主模态通过使用全局优化和注意力机制选择和净化的输入级的模态。为了解决第二个问题，双向短连接融合模块被用来优化HRFormer的输出特征，从而在输出层面上增强对象的详细表示。该模型首先引入一个辅助流来提取补充模态的特征提取。然后，在每个多分辨率分支的开始处将特征注入到主模态中。接下来，应用HRFormer来实现转发传播。最后通过特征内和特征间交互Transformers对不同分辨率的输出特征进行聚合。所提出的模型的应用结果在驱动双模态SOD任务，例如，RGB-D、RGB-T和光场SOD.https://github.com/liuzywen/HRTransNet
英文摘要：
The High-Resolution Transformer (HRFormer) can maintain high-resolution representation and share global receptive fields. It is friendly towards salient object detection (SOD) in which the input and output have the same resolution. However, two critical problems need to be solved for two-modality SOD. One problem is two-modality fusion. The other problem is the HRFormer output's fusion. To address the first problem, a supplementary modality is injected into the primary modality by using global optimization and an attention mechanism to select and purify the modality at the input level. To solve the second problem, a dual-direction short connection fusion module is used to optimize the output features of HRFormer, thereby enhancing the detailed representation of objects at the output level. The proposed model, named HRTransNet, first introduces an auxiliary stream for feature extraction of supplementary modality. Then, features are injected into the primary modality at the beginning of each multi-resolution branch. Next, HRFormer is applied to achieve forwarding propagation. Finally, all the output features with different resolutions are aggregated by intra-feature and inter-feature interactive transformers. Application of the proposed model results in impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T, and light field SOD.https://github.com/liuzywen/HRTransNet
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
