Results 11 to 20 of about 1,221,513 (328)
Survey of Image Semantic Segmentation Methods Based on Deep Neural Network
Jisuanji kexue yu tansuo, 2021 Image semantic segmentation is a hot research topic in the field of computer vision in recent years. With the rise of deep learning technology, image semantic segmentation and deep learning technology are integrated and developed, which has made ...
XU Hui, ZHU Yuhua, ZHEN Tong, LI Zhihui
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SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation [PDF]
Neural Information Processing Systems, 2022 We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information.
Meng-Hao Guo +5 more
semanticscholar +1 more source
2D Semantic Segmentation: Recent Developments and Future Directions
Future Internet, 2023 Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content.
Yu Guo +3 more
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YUTO SEMANTIC: A LARGE SCALE AERIAL LIDAR DATASET FOR SEMANTIC SEGMENTATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Creating virtual duplicates of the real world has garnered significant attention due to its applications in areas such as autonomous driving, urban planning, and urban mapping.
S. Yoo, C. Ko, G. Sohn, H. Lee
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GroupViT: Semantic Segmentation Emerges from Text Supervision [PDF]
Computer Vision and Pattern Recognition, 2022 Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision ...
Jiarui Xu +6 more
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Fully convolutional networks for semantic segmentation [PDF]
Computer Vision and Pattern Recognition, 2014 Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation.
Evan Shelhamer +2 more
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Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP [PDF]
Computer Vision and Pattern Recognition, 2022 Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-
Feng Liang +8 more
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Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels [PDF]
Computer Vision and Pattern Recognition, 2022 The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most ...
Yuchao Wang +8 more
semanticscholar +1 more source
IEEE Access, 2023 With the rapid development of deep learning technology, the accuracy of image semantic segmentation tasks has been greatly improved. However, indoor RGB-D semantic segmentation remains a challenging problem because of the complexity of indoor ...
Yunping Zheng +5 more
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HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation [PDF]
European Conference on Computer Vision, 2022 Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain (e.g. synthetic data) to the target domain (e.g. real-world data) without requiring further annotations on the target domain.
Lukas Hoyer, Dengxin Dai, L. Gool
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