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| Generalized Zero-Shot Image Classification Based on Reconstruction Contrast |
| XU Rui1, SHAO Shuai2, CAO Weijia3, LIU Baodi1, TAO Dapeng4, LIU Weifeng1 |
1. College of Control Science and Engineering, China University of Petroleum(East China), Qingdao 266580;
2. Research Institute of Basic Theories, Zhejiang Laboratory, Hangzhou 311121;
3. National Engineering Research Center of Remote Sensing Satellite Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094;
4. School of Information Science and Engineering, Yunnan University, Yunnan 650500 |
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Abstract In generalized zero-shot image classification, generative models are often exploited to reconstruct visual or semantic information for further learning. However, the representation performance of the methods based on variational autoencoders is poor due to the underutilization of the reconstructed samples. Therefore, a generalized zero-shot image classification model based on reconstruction and contrastive learning is proposed. Firstly, two variational self-encoders are utilized to encode visual information and semantic information into low dimensional latent vectors of the same dimension, and then the latent vectors are decoded into two modes respectively. Next, the project modules are utilized to project both the original visual information and the visual information reconstructed from semantic modal latent vectors. Then, reconstruction contrastive learning is performed to learn the features after projection. The reconstruction performance of the encoder is maintained, the discriminative performance of the encoder is enhanced, and the application ability of pre-training features on the generalized zero-shot task is improved by the proposed method. The effectiveness of the proposed model is verified on four benchmark datasets.
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Received: 20 May 2022
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| Fund:National Natural Science Foundation of China(No.61671480), Major Scientific and Technological Projects of CNPC(No.ZD2019-183-008), Open Project of the National Laboratory of Pattern Recognition (NLPR)(No.202000009), Graduate Innovation Project of China University of Petroleum(East China)(No.YCX2021123) |
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Corresponding Authors:
LIU Weifeng, Ph.D., professor. His research interests include pa-ttern recognition and machine learning.
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| About author:: XU Rui, Ph.D. candidate. Her research interests include few-shot learning and zero-shot learning.SHAO Shuai, Ph.D. His research inte-rests include dictionary learning and few-shot learning.CAO Weijia, Ph.D. assistant professor. Her research interests include image encryption, image compression and image classification.LIU Baodi, Ph.D., associate professor. His research interests include computer vision and machine learning.TAO Dapeng, Ph.D., professor. His research interests include machine learning, computer vision and cloud computing. |
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2022, Vol. 35 
