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Learning Intra-View and Cross-View Geometric Knowledge for Stereo Matching
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Geometric knowledge has been shown to be beneficial for the stereo matching task. However, prior attempts to in-tegrate geometric insights into stereo matching algorithms have largely focused on geometric knowledge from single images while crucial cross-view factors such as occlusion and matching uniqueness have been overlooked. To address this gap, we propose a novel Intra-view and Cross-view Geo...Show More
Stereo-LiDAR fusion is often used for autonomous systems such as self-driving cars as the two modalities are complementary to each other. Existing stereo-LiDAR fusion methods are mostly at feature level or outcome level, without considering the uncertainty of the depth estimation in each modality. To this end, we propose a holistic and contextual evidential stereo-LiDAR fusion network (HCENet) for...Show More
ELFNet: Evidential Local-global Fusion for Stereo Matching
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation. Additionally, effectively leveraging multi-scale and multi-view knowledge of stereo pairs remains unexplored. In this paper, we introduce the Evidential Local-global Fusion (ELF) framework for stereo matching, which endows bo...Show More
Adaptive Cross-Feature Fusion Network With Inconsistency Guidance for Multi-Modal Brain Tumor Segmentation
Guanghui Yue;Guibin Zhuo;Tianwei Zhou;Weide Liu;Tianfu Wang;Qiuping Jiang
IEEE Journal of Biomedical and Health Informatics
Year: 2023 | Early Access Article |
Cited by: Papers (1)
In the context of contemporary artificial intelligence, increasing deep learning (DL) based segmentation methods have been recently proposed for brain tumor segmentation (BraTS) via analysis of multi-modal MRI. However, known DL-based works usually directly fuse the information of different modalities at multiple stages without considering the gap between modalities, leaving much room for performa...Show More
Towards Explainable Recommendation Via Bert-Guided Explanation Generator
Huijing Zhan;Ling Li;Shaohua Li;Weide Liu;Manas Gupta;Alex C. Kot
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Explainable recommender system has recently drawn increasing attention due to its capability of providing justification to recommendation. Rather than focusing on certain topics or specific item features, the explanation generated by existing works are too general without the guidance of aspects. However, such information is not given in the practical scenario. To address this issue, we propose a ...Show More
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