Results 21 to 30 of about 76,480 (326)
Sensors, 2022 The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield.
Shrinidhi Adke +3 more
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Weakly supervised foreground learning for weakly supervised localization and detection
Pattern Recognition, 2023 Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy. Hence, weakly supervised tasks, including weakly supervised object localization~(WSOL) and detection~(WSOD), have recently received attention in the computer vision community.
Chen-Lin Zhang, Yin Li, Jianxin Wu
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Weakly Supervised Correspondence Learning
2022 International Conference on Robotics and Automation (ICRA), 2022 Correspondence learning is a fundamental problem in robotics, which aims to learn a mapping between state, action pairs of agents of different dynamics or embodiments. However, current correspondence learning methods either leverage strictly paired data -- which are often difficult to collect -- or learn in an unsupervised fashion from unpaired data ...
Wang, Zihan +3 more
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Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
Applied Sciences, 2023 Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain.
Yating Kong +3 more
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OVERVIEW OF COMPUTER VISION SUPERVISED LEARNING TECHNIQUES FOR LOW-DATA TRAINING [PDF]
Journal of Engineering Science (Chişinău), 2020 In the age of big data and machine learning the costs to turn the data into fuel for the algorithms is prohibitively high. Organizations that can train better models with fewer annotation efforts will have a competitive edge.
BURLACU, Alexandru
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Safe Weakly Supervised Learning [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Weakly supervised learning (WSL) refers to learning from a large amount of weak supervision data. This includes i) incomplete supervision (e.g., semi-supervised learning); ii) inexact supervision (e.g., multi-instance learning) and iii) inaccurate supervision (e.g., label noise learning). Unlike supervised learning which typically achieves performance
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Weakly Supervised Learning for Textbook Question Answering [PDF]
IEEE Transactions on Image Processing, 2021 Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multi-modal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for
Jie Ma +5 more
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MetaFL: Metamorphic fault localisation using weakly supervised deep learning
IET Software, 2023 Deep‐Learning‐based Fault Localisation (DLFL) leverages deep neural networks to learn the relationship between statement behaviour and program failures, showing promising results.
Lingfeng Fu +5 more
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International Journal of Applied Earth Observations and Geoinformation, 2022 Semantic segmentation of large-scale mobile laser scanning (MLS) point clouds is essential for urban scene understanding. However, most of the existing semantic segmentation methods require a large quantity of labeled data, which are labor-intensive and ...
Xiangda Lei +7 more
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Agriculture, 2020 Rice is one of the most important staple food sources worldwide. Effective and cheap monitoring of rice planting areas is demanded by many developing countries.
Mo Wang, Jing Wang, Li Chen
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