ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation [PDF]
, 2016 We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies.
Bengio, Yoshua +7 more
core +2 more sources
Semi-Supervised Semantic Segmentation With Cross-Consistency Training [PDF]
Computer Vision and Pattern Recognition, 2020 In this paper, we present a novel cross-consistency based semi-supervised approach for semantic segmentation. Consistency training has proven to be a powerful semi-supervised learning framework for leveraging unlabeled data under the cluster assumption ...
Yassine Ouali, C. Hudelot, Myriam Tami
semanticscholar +1 more source
JSMNET: IMPROVING INDOOR POINT CLOUD SEMANTIC AND INSTANCE SEGMENTATION THROUGH SELF-ATTENTION AND MULTISCALE FUSION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 The semantic understanding of indoor 3D point cloud data is crucial for a range of subsequent applications, including indoor service robots, navigation systems, and digital twin engineering. Global features are crucial for achieving high-quality semantic
S. Xu, S. Xu, Z. Zhang, Z. Zhang
doaj +1 more source
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation [PDF]
Computer Vision and Pattern Recognition, 2020 Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (CAM).
Yude Wang +4 more
semanticscholar +1 more source
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview [PDF]
arXiv, 2022 Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. Conventional learning-based visual semantic segmentation approaches count heavily on large-scale training data with dense annotations ...
arxiv
Semantic Part Segmentation using Compositional Model combining Shape and Appearance [PDF]
, 2014 In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often have similar appearance and ...
Wang, Jianyu, Yuille, Alan
core +2 more sources
International Journal of Digital EarthIn the field of remote sensing, using a large amount of labeled image data to supervise the training of fully convolutional networks for the semantic segmentation of images is expensive.
Zenan Yang +4 more
doaj +1 more source
Research on Semantic Segmentation Algorithm for Infrared Moving Targets in Complex Backgrounds [PDF]
Hangkong bingqiThe semantic segmentation of infrared weak targets relies more on detailed texture features, and deeper network architecture are not suitable for semantic segmentation of infrared targets, making it difficult to accurately segment weak targets from ...
Ji Haoyu, Meng Weihua, Zhang Xinchao, Duan Jingfei, Zhang Meng
doaj +1 more source
High precision semantic segmentation based on multi-level feature fusion
Xi'an Gongcheng Daxue xuebao, 2021 In order to solve the problems of fuzzy edge segmentation and imprecise segmentation of small objects in image semantic segmentation, a high-precision semantic segmentation method was proposed.
Xiaohua WANG +3 more
doaj +1 more source
Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation [PDF]
, 2019 Training a Convolutional Neural Network (CNN) for semantic segmentation typically requires to collect a large amount of accurate pixel-level annotations, a hard and expensive task. In contrast, simple image tags are easier to gather.
Baptista-Ríos, Marcos +2 more
core +2 more sources