Results 241 to 250 of about 11,177,407 (275)

Zero-Shot Image Classification Based on Deep Feature Extraction

IEEE Transactions on Cognitive and Developmental Systems, 2018
The attribute-based zero-shot learning methods generally use low-level features of images to train attribute classifiers, and the corresponding classification accuracy heavily depends on specific low-level features. Because deep networks can automatically extract features from original unlabeled images and the extracted features can better represent ...
Chen Chen, Z Jane Wang, Xuesong Wang
exaly   +2 more sources

Zero-Shot Image Classification via Coupled Discriminative Dictionary Learning

Communications in Computer and Information Science, 2017
In this paper, we propose a Coupled Discriminative Dictionary Learning framework to tackle the zero-shot image classification problem. Instead of the original attribute vectors and sample feature vectors, we use their corresponding sparse coefficients attained from sparse coding to do the classification.
Wu Songsong
exaly   +3 more sources

Evaluation of the CLIP Architecture for Zero-Shot Image Classification on the Intel Image Classification Dataset

Indonesian Journal of Data Risk Research
The performance evaluation of various architectures in the Contrastive Language–Image Pre-training (CLIP) model was conducted in a zero-shot image classification scenario. Image classification was performed using the Intel Image Classification Dataset, which consists of 3000 images representing several environmental categories.
Ade Lailani, Rohmi Dyah Astuti, Christyan Tamaro Nadeak   +2 more
openaire   +2 more sources

Fusing spatial and frequency features for compositional zero-shot image classification

Expert Systems With Applications
© 2024 Elsevier LtdCompositional Zero-Shot Learning (CZSL) is a particular Zero-Shot Learning (ZSL) task that aims to utilize known concepts (e.g., states and objects) to identify novel state-object compositions for Image Classification. Previous works have primarily focused on disentangling concept compositions or exploring the complex interactions ...
Haofeng Zhang, Wankou Yang, Qiaolin Ye   +2 more
exaly   +4 more sources

Zero-shot image classification based on factor space

International Journal of Web Engineering and Technology, 2021
Image recognition technology has become a popular research topic with the progress and development of big data and artificial intelligence technology. This study applies factor space to the semantic embedding space, maintains the consistency between the high-level semantic and low-level image feature spaces, and establishes a direct connection between ...
Shijie Guan, Qixue Guan, Anqi Yin
openaire   +2 more sources

Zero-shot image classification based on attribute

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 2017
In the image classification task, traditional model can only recognize annotated image samples, but class labels can't involve all the object categories. In order to reduce the dependence on the labels and recognize unannotated object samples, this paper proposes zero-shot image classification based on attribute.
Wei Zhang, Wenbai Chen, Xiangfeng Chen, Hu Han 0001   +3 more
openaire   +2 more sources

Medical Image Classification Using Generalized Zero Shot Learning

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021
In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for: 1) selecting anchor vectors of ...
Mahapatra, Dwarikanath, Bozorgtabar, Behzad, Ge, Zongyuan   +2 more
openaire   +2 more sources

Zero-Shot Image Classification via Consistent Subspace Learning

Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things, 2020
Recent years have witnessed great advances in image classification due to the availability of high-volume data and the development of deep neural networks. Nevertheless, the classification performance strongly depends on abundant training samples for the specific category, and there is no ability to recognize categories that do not appear during the ...
Shiwei Chen, Hongyun Zhang
openaire   +2 more sources

Zero-Shot Image Classification Method Based on Attribute Weighting

2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), 2019
In order to solve the defect that the attribute has the same effect during the zero-shot classification decision, the objective weighting method is introduced to learn the attribute weights. Firstly, the convolutional neural network is used to encode the image information, and the two full connected layers are used to encode the semantic attributes ...
Wenbai Chen, Xiangfeng Chen, Chang Liu, Hao Wu, Denghua Li   +4 more
openaire   +2 more sources

Zero-shot image classification via Visual–Semantic Feature Decoupling

Multimedia Systems
Haojie Li
exaly   +2 more sources