These two attention-based modules explore the complementarity of modalities and scales, narrowing the gaps and avoiding complex structures for road segmentation ...

Dec 31, 2023 · Learning multi-modal scale-aware attentions for efficient and robust road segmentation. Open Webpage. Yunjiao Zhou, Jianfei Yang, Haozhi Cao, Zhaoyang Zeng, ...

Multi-modal fusion has proven to be beneficial to road segmentation in autonomous driving, where depth is commonly used as complementary data for RGB images to ...

Nov 14, 2024 · Title: Learning Multi-Modal Scale-Aware Attentions for Efficient and Robust Road Segmentation. · Authors: Yunjiao Zhou, Jianfei Yang, Haozhi Cao, ...

Learning multi-modal scale-aware attentions for efficient and robust road segmentation. Y Zhou, J Yang, H Cao, Z Zeng, H Zou, L Xie. Unmanned Systems 12 (2) ...

To address these issues, we propose a Multi-modal Scale-aware Attention Network (MSAN) to fuse RGB and Depth data effectively via a novel transformer-based ...

Multi-modal multi-task visual understanding foundation models (MM-VUFMs) effectively process and fuse data from diverse modalities and simultaneously handle ...

Dec 2, 2024 · In this paper, we introduce VisToG, a novel grouping mechanism that leverages the capabilities of pre-trained vision encoders to group similar ...

The proposed attention model learns to weight the multi-scale features according to the object scales presented in the image (e.g., the model learns to put ...

In this paper, we introduce SA2-Net, an attention-guided method that leverages multi-scale feature learning to effectively handle diverse structures within ...