Results 31 to 40 of about 12,744 (261)
Weakly Supervised Learning Approach for Implicit Aspect Extraction
Information, 2023 Aspect-based sentiment analysis (ABSA) is a process to extract an aspect of a product from a customer review and identify its polarity. Most previous studies of ABSA focused on explicit aspects, but implicit aspects have not yet been the subject of much ...
Aye Aye Mar +2 more
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Constrained Labeling for Weakly Supervised Learning
CoRR, 2020 Accepted at UAI ...
Chidubem Arachie, Bert Huang
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Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
Frontiers in Physiology, 2022 The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis.
Jianguo Zhou +8 more
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Weakly Supervised Learning of Affordances
CoRR, 2016 Localizing functional regions of objects or affordances is an important aspect of scene understanding. In this work, we cast the problem of affordance segmentation as that of semantic image segmentation. In order to explore various levels of supervision, we introduce a pixel-annotated affordance dataset of 3090 images containing 9916 object instances ...
Abhilash Srikantha, Juergen Gall
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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 0001 +5 more
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Instance-Level Contrastive Learning for Weakly Supervised Object Detection
Sensors, 2022 Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs ...
Ming Zhang, Bing Zeng
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Knodle: Modular Weakly Supervised Learning with PyTorch [PDF]
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), 2021 Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture. In this work, we introduce Knodle, a software framework that treats weak data annotations, deep learning models, and methods for improving ...
Sedova, Anastasiia +3 more
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Weakly supervised classification in high energy physics
Journal of High Energy Physics, 2017 As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations.
Lucio Mwinmaarong Dery +3 more
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Weakly Supervised Action Selection Learning in Video [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required.
Junwei Ma +3 more
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Remote Sensing, 2020 The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information ...
Jie Chen +4 more
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