Zero-Shot Topic Labeling for Hazard Classification
Information, 2022 Topic classification is the task of mapping text onto a set of meaningful labels known beforehand. This scenario is very common both in academia and industry whenever there is the need of categorizing a big corpus of documents according to set custom ...
Andrea Rondinelli +2 more
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Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
Applied Sciences, 2022 Deep learning-based methods succeed in remote sensing scene classification (RSSC). However, current methods require training on a large dataset, and if a class does not appear in the training set, it does not work well.
Zihao Li +4 more
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Survey of Zero-Shot Image Classification
Jisuanji kexue yu tansuo, 2021 It is time-consuming and laborious to manually label a large number of samples, and samples from some rare classes are difficult to obtain. Therefore, the zero-shot image classification has become a research hotspot in the computer vision field. Firstly,
LIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai
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Evaluation of large language models for VI-RADS reports: a comparative analysis of zero-shot and few-shot prompting [PDF]
BMC Medical ImagingIntroduction Accurate preoperative staging is vital in bladder cancer management, particularly for assessing muscle invasion. Multiparametric MRI (mpMRI) combined with the Vesical Imaging–Reporting and Data System (VI-RADS) offers a non-invasive and ...
Ahmet Halis, Deniz Celiker
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Zero-Shot Learning for Cross-Lingual News Sentiment Classification
Applied Sciences, 2020 In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment ...
Andraž Pelicon +4 more
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Evaluating few-shot prompting for spectrogram-based lung sound classification using a multimodal language model. [PDF]
PLOS Digital HealthTraditional deep learning models for lung sound analysis require large, labeled datasets, whereas multimodal large language models (LLMs) may offer a flexible, prompt-based alternative.
Nicholas Dietrich +2 more
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Zero-Shot Image Classification Based on Improved Variational Auto-encoder
Taiyuan Ligong Daxue xuebao, 2021 In the process of zero-shot image classification, problems such as high acquisition cost for samples of known categories and domain drift were addressed.
Zhen CAO, Hongwei XIE
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RS-CLIP: Zero shot remote sensing scene classification via contrastive vision-language supervision
International Journal of Applied Earth Observations and Geoinformation, 2023 Zero-shot remote sensing scene classification aims to solve the scene classification problem on unseen categories and has attracted numerous research attention in the remote sensing field.
Xiang Li +3 more
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EntailClass: A Classification Approach to EntailSum and End-to-End Document Extraction, Identification, and Evaluation [PDF]
Journal of Universal Computer Science, 2023 The novelty of zero-shot text classification can address the fundamental challenge of the lack of labeled training data. With the current plethora of multidisciplinary, unstandardized text data, scalable classification models favor unsupervised methods ...
Purvaja Balaji +2 more
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Latent Embeddings for Zero-Shot Classification [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method augments the state-of-the-art bilinear compatibility model by incorporating latent variables.
Yongqin Xian +5 more
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