Category-guided graph convolution network for semantic segmentation
Contextual information has been widely used to improve results of semantic segmentation.
However, most approaches investigate contextual dependencies through self-attention and …
However, most approaches investigate contextual dependencies through self-attention and …
Loss functions in the era of semantic segmentation: A survey and outlook
Semantic image segmentation, the process of classifying each pixel in an image into a
particular class, plays an important role in many visual understanding systems. As the …
particular class, plays an important role in many visual understanding systems. As the …
[HTML][HTML] Research on the method of foreign object detection for railway tracks based on deep learning
S Ning, F Ding, B Chen - Sensors, 2024 - mdpi.com
Addressing the limitations of current railway track foreign object detection techniques, which
suffer from inadequate real-time performance and diminished accuracy in detecting small …
suffer from inadequate real-time performance and diminished accuracy in detecting small …
Spike-BRGNet: Efficient and Accurate Event-based Semantic Segmentation with Boundary Region-Guided Spiking Neural Networks
Event-based semantic segmentation in traffic scenes has attracted considerable attention in
autonomous driving systems due to the advantages of event cameras such as high dynamic …
autonomous driving systems due to the advantages of event cameras such as high dynamic …
[HTML][HTML] BAFormer: A Novel Boundary-Aware Compensation UNet-like Transformer for High-Resolution Cropland Extraction
Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery
has become a crucial technique in land surveys. Cropland is highly heterogeneous and …
has become a crucial technique in land surveys. Cropland is highly heterogeneous and …
Learning and aggregating principal semantics for semantic edge detection in images
Learning features that contain both local variations and non-local semantics is crucial yet
challenging for Semantic Edge Detection (SED), which involves the joint localization and …
challenging for Semantic Edge Detection (SED), which involves the joint localization and …
Soft labelling for semantic segmentation: Bringing coherence to label down-sampling
In semantic segmentation, training data down-sampling is commonly performed due to
limited resources, the need to adapt image size to the model input, or improve data …
limited resources, the need to adapt image size to the model input, or improve data …
Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation
Recent attention has been devoted to the pursuit of learning semantic segmentation models
exclusively from image tags, a paradigm known as image-level Weakly Supervised …
exclusively from image tags, a paradigm known as image-level Weakly Supervised …
Difference-complementary Learning and Label Reassignment for Multimodal Semi-Supervised Semantic Segmentation of Remote Sensing Images
W Han, W Jiang, J Geng, W Miao - IEEE Transactions on Image …, 2025 - ieeexplore.ieee.org
The feature fusion of optical and Synthetic Aperture Radar (SAR) images is widely used for
semantic segmentation of multimodal remote sensing images. It leverages information from …
semantic segmentation of multimodal remote sensing images. It leverages information from …
Layer-Specific Knowledge Distillation for Class Incremental Semantic Segmentation
Recently, class incremental semantic segmentation (CISS) towards the practical open-world
setting has attracted increasing research interest, which is mainly challenged by the well …
setting has attracted increasing research interest, which is mainly challenged by the well …