相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article
Computer Science, Artificial Intelligence
Yiqiao Qiu et al.
Summary: In this study, a new knowledge transfer method was proposed to alleviate the catastrophic forgetting issue in continuous semantic segmentation. This method captures the relationships between elements within each image using self-attention maps in a Transformer-style segmentation model. Extensive evaluations show that the proposed method outperforms state-of-the-art solutions when combined with widely adopted strategies.
PATTERN RECOGNITION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Songhua Liu et al.
Summary: In this paper, the authors propose a slimmable dataset condensation method that extracts a smaller synthetic dataset based on previous condensation results. They study the limitations of existing methods in a successive compression setting and identify two key factors. To address these issues, they introduce a novel training objective and a significance-aware parameterization method. Extensive comparisons and experiments demonstrate the superiority of their method over existing methods.
2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR
(2023)
Article
Computer Science, Artificial Intelligence
Quan Zhou et al.
Summary: This paper introduces a novel encoder-decoder architecture called CENet for semantic segmentation, which achieves superior performance on two widely-used semantic segmentation datasets and obtains promising results on instance segmentation and biological segmentation tasks.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Jie Wei et al.
Summary: A cascaded nested network (CaNes-Net) is proposed for brain MR image segmentation at 3T, trained by tissue labels delineated from 7T images, reducing segmentation errors caused by misalignment and improving accuracy substantially.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Wei Wu et al.
Summary: RGB-T semantic segmentation has gained attention for its robustness under challenging illumination. This paper proposes a Complementarity-aware Cross-modal Feature Fusion Network (CCFFNet) that selects and fuses complementary information from RGB and thermal features. Experimental results show that the proposed model outperforms state-of-the-art models and can be easily applied to multi-modal semantic segmentation.
PATTERN RECOGNITION
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Qihang Yu et al.
Summary: This paper presents a transformer-based framework for panoptic segmentation, called Clustering Mask Transformer (CMT-DeepLab), which utilizes a CMT layer to compute pixel clustering based on feature affinity and generate denser and more consistent cross-attention for the final segmentation task. Experimental results show that this method significantly improves performance compared to prior art.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Bowen Cheng et al.
Summary: Masked-attention Mask Transformer (Mask2Former) is a new architecture capable of addressing any image segmentation task and outperforms specialized architectures on multiple datasets.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Article
Computer Science, Information Systems
Wujie Zhou et al.
Summary: In this paper, a novel multiscale feature fusion and enhancement network (MFFENet) is proposed for accurate parsing of RGB-thermal urban road scenes. By incorporating multi-label supervision and a spatial attention mechanism module, the MFFENet outperforms similar high-performing methods and emphasizes foreground objects in the scene.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Automation & Control Systems
Yuxiang Sun et al.
Summary: Semantic segmentation of urban scenes is crucial for autonomous driving applications, and recent advancements in deep learning have led to improved performance. However, traditional networks using single-modal sensory data may struggle in challenging lighting conditions. This article introduces the FuseSeg network, which fuses RGB and thermal data to achieve superior segmentation performance in urban scenes. The network outperforms state-of-the-art networks and can be easily implemented using various deep learning frameworks.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jiangtao Xu et al.
Summary: The proposed deep learning model, AFNet, utilizes attention mechanism to calculate spatial correlation between features from different spectra, improving the accuracy and visual definition of multi-spectral semantic segmentation.
PATTERN RECOGNITION LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Alejandro Lopez-Cifuentes et al.
PATTERN RECOGNITION
(2020)
Article
Computer Science, Information Systems
Qiurui Wang et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2019)
Article
Robotics
Yuxiang Sun et al.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2019)