Results 31 to 40 of about 16,806 (262)
Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition
Sensors, 2019 In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to
Moe Matsuki, Paula Lago, Sozo Inoue
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A Survey of Zero-Shot Image Classification: Concepts, Developments, and Challenges
Tsinghua Science and TechnologyZero-shot learning is gaining increasing attention in the social computing community, primarily because it can enable models to effectively perform classification or regression tasks when new concepts continually emerge while lacking sufficient training ...
Shuai Xu +5 more
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Locality and compositionality in zero-shot learning
CoRR, 2019 Published at ICLR ...
Tristan Sylvain +2 more
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Generalized Continual Zero-Shot Learning
CoRR, 2020 Recently, zero-shot learning (ZSL) emerged as an exciting topic and attracted a lot of attention. ZSL aims to classify unseen classes by transferring the knowledge from seen classes to unseen classes based on the class description. Despite showing promising performance, ZSL approaches assume that the training samples from all seen classes are available
Chandan Gautam +3 more
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Synthesizing Samples for Zero-shot Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Zero-shot learning (ZSL) is to construct recognition models for unseen target classes that have no labeled samples for training. It utilizes the class attributes or semantic vectors as side information and transfers supervision information from related source classes with abundant labeled samples. Existing ZSL approaches adopt an intermediary embedding
Yuchen Guo +3 more
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Generalized Zero Shot Learning via Synthesis Pseudo Features
IEEE Access, 2019 Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes.
Chuanlong Li +5 more
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Semantic Autoencoder for Zero-Shot Learning [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g.~attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g.~attribute prediction) or classification. When applied to test data, which in
Elyor Kodirov +2 more
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Synthesized Classifiers for Zero-Shot Learning [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided. We propose to tackle this problem from the perspective of manifold learning. Our main
Soravit Changpinyo +3 more
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ActuatorsPath planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process.
Liwei Mei, Pengjie Xu
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Fabric Recognition Using Zero-Shot Learning
Tsinghua Science and Technology, 2019 In this work, we use a deep learning method to tackle the Zero-Shot Learning (ZSL) problem in tactile material recognition by incorporating the advanced semantic information into a training model. Our main technical contribution is our proposal of an end-
Feng Wang +3 more
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