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ACM Transactions on Graphics, 2018
Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introduce
Yağız Aksoy +4 more
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Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introduce
Yağız Aksoy +4 more
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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
The availability of GIS (Geographical Information System) databases for many urban areas, provides a valuable source of information for improving the performance of many computer vision tasks. In this paper, we propose a method which leverages information acquired from GIS databases to perform semantic segmentation of the image alongside with geo ...
Ardeshir, Shervin +2 more
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The availability of GIS (Geographical Information System) databases for many urban areas, provides a valuable source of information for improving the performance of many computer vision tasks. In this paper, we propose a method which leverages information acquired from GIS databases to perform semantic segmentation of the image alongside with geo ...
Ardeshir, Shervin +2 more
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Covariance Attention for Semantic Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020The dependency between global and local information can provide important contextual cues for semantic segmentation. Existing attention methods capture this dependency by calculating the pixel wise correlation between the learnt feature maps, which is of high space and time complexity.
Yazhou, Liu +3 more
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Prototype-Based Semantic Segmentation
IEEE Transactions on Pattern Analysis and Machine IntelligenceDeep learning based semantic segmentation solutions have yielded compelling results over the preceding decade. They encompass diverse network architectures (FCN based or attention based), along with various mask decoding schemes (parametric softmax based or pixel-query based).
Tianfei Zhou, Wenguan Wang
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Semantic Customers’ Segmentation
2019Many approaches have been proposed to allow customers’ segmentation in retail sector. However, very few contributions exploit the existing semantics links that may exist between objects and resulting groups. The aim of this paper is to overcome this drawback by using semantic similarity measures (SSM) in customers’ segmentation to provide clusters ...
Jocelyn Poncelet +3 more
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Semantic segmentation based on semantic edge optimization
2021 International Conference on Electronic Information Engineering and Computer Science (EIECS), 2021Semantic segmentation based on deep learning is to extract semantic features by convolution, and then classify each pixel. In the process of feature extraction, the subsampled operation used to expand the receptive field will cause a large amount of detail information loss, resulting in the loss of small-scale objects in the image and blurred ...
Hao Hu +3 more
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Semantic Segmentation For Aerial Images
International Journal of Research Publication and ReviewsSemantic segmentation of aerial imagery plays a critical role in modern urban planning, environmental monitoring, and the development of smart cities. This project presents an interactive webbased application that performs semantic segmentation on high-resolution aerial images using a deep learning-based U-Net model.
Dr. B. Harika, M. Bharath, K. Himneesh
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2011
Semantic object segmentation is to label each pixel in an image or a video sequence to one of the object classes with semantic meanings. It has drawn a lot of research interest because of its wide applications to image and video search, editing and compression.
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Semantic object segmentation is to label each pixel in an image or a video sequence to one of the object classes with semantic meanings. It has drawn a lot of research interest because of its wide applications to image and video search, editing and compression.
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Semantic AutoSAM: Self-Prompting Segment Anything Model for Semantic Segmentation of Medical Images
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)Segment Anything Model (SAM) is a foundation model that can be prompted with sparse prompts, like boxes or points, and dense prompts such as masks. SAM outputs binary masks based on the given prompts but lacks semantic understanding as it doesn't output the class of the predicted mask.
Assefa S, Wahd +3 more
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Heterogeneous Federated Semantic Segmentation
NeurocomputingChen Zhang +4 more
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