Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …

Recently, RGB-Thermal based perception has shown significant advances. Thermal
information provides useful clues when visual cameras suffer from poor lighting conditions …

Recent achievements in scene understanding have benefited considerably from the rapid
development of convolutional neural networks. However, improvements of scene …

We present DFormer, a novel RGB-D pretraining framework to learn transferable
representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) …

Combining color (RGB) images with thermal images can facilitate semantic segmentation of
poorly lit urban scenes. However, for RGB-thermal (RGB-T) semantic segmentation, most …

Deep learning has become a popular method for studying the semantic segmentation of
high-resolution remote sensing images (HRRSIs). Existing methods have adopted …

Cross-modal text-image retrieval in remote sensing (RS) provides a flexible retrieval
experience for mining useful information from RS repositories. However, existing methods …

Salient object detection (SOD) is an important task in computer vision that aims to identify
visually conspicuous regions in images. RGB-Thermal SOD combines two spectra to …

Owing to rapid developments in driverless technologies, vision tasks for unmanned vehicles
have gained considerable attention, particularly in multimodal-based urban scene parsing …

RGB-D salient object detection (SOD) has gained tremendous attention in recent years. In
particular, transformer has been employed and shown great potential. However, existing …