Results 11 to 20 of about 12,717 (259)
Zero-shot Image Classification Method Based on Discriminator Feedback
Journal of Harbin University of Science and Technology, 2023 Zero-shot learning (ZSL) strives to classify unseen categories for which no data is available during training.At present, among generative methods, zero-shot learning based on joint generative model VAEGAN is a research hotspot.On this basis, we propose ...
FAN Yufei, DING Bo, HE Yongjun
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Generalized Zero-Shot Audio-to-Intent Classification
2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2023 Spoken language understanding systems using audio-only data are gaining popularity, yet their ability to handle unseen intents remains limited. In this study, we propose a generalized zero-shot audio-to-intent classification framework with only a few sample text sentences per intent.
Veera Raghavendra Elluru +4 more
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Prognostication of Unseen Objects using Zero-Shot Learning with a Complete Case Analysis [PDF]
Interdisciplinary Description of Complex Systems, 2022 Generally, for a machine learning model to perform well, the data instances on which the model is being trained have to be relevant to the use case.
Srinivasa L. Chakravarthy +1 more
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Performance Variability in Zero-Shot Classification
LatinX in AI at Neural Information Processing Systems Conference 2020, 2020 Zero-shot classification (ZSC) is the task of learning predictors for classes not seen during training. Although the different methods in the literature are evaluated using the same class splits, little is known about their stability under different class partitions.
Matías Molina, Jorge Sánchez
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Generating Visual Representations for Zero-Shot Classification [PDF]
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017 This paper addresses the task of learning an image clas-sifier when some categories are defined by semantic descriptions only (e.g. visual attributes) while the others are defined by exemplar images as well. This task is often referred to as the Zero-Shot classification task (ZSC).
Bucher, Maxime +2 more
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Zero-Shot Text Classification with Self-Training
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 9 pages, 5 figures; To be published in EMNLP ...
Ariel Gera +5 more
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Zero-shot Relation Classification as Textual Entailment [PDF]
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), 2018 We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classification by exploiting relation descriptions, (ii) utilize existing textual entailment models, and (iii) leverage readily available ...
Abiola Obamuyide, Andreas Vlachos 0001
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Zero-Shot Audio Classification Via Semantic Embeddings [PDF]
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021 Submitted to Transactions on Audio, Speech and Language ...
Xie Huang, Virtanen Tuomas
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Gaze Embeddings for Zero-Shot Image Classification [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have a natural ability to judge class membership.
Nour Karessli +3 more
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Company classification using zero-shot learning
CoRR, 2023 In recent years, natural language processing (NLP) has become increasingly important in a variety of business applications, including sentiment analysis, text classification, and named entity recognition. In this paper, we propose an approach for company classification using NLP and zero-shot learning. Our method utilizes pre-trained transformer models
Maryan Rizinski +5 more
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