Abstract Small-sample classification is a challenging problem in computer vision. In this work, we show how to efficiently and effectively utilize semantic information of the annotations to improve the performance… Click to show full abstract
Abstract Small-sample classification is a challenging problem in computer vision. In this work, we show how to efficiently and effectively utilize semantic information of the annotations to improve the performance of small-sample classification. First, we propose an image-text dual neural network to improve the classification performance on small-sample datasets. The proposed model consists of two sub-models, an image classification model and a text classification model. After training the sub-models separately, we design a novel method to fuse the two sub-models rather than simply combine their results. Our image-text dual neural network aims to utilize the text information to overcome the training problem of deep models on small-sample datasets. Then, we propose to incorporate a decision strategy into the image-text dual neural network to further improve the performance of our original model on few-shot datasets. To demonstrate the effectiveness of the proposed models, we conduct experiments on the LabelMe and UIUC-Sports datasets. Experimental results show that our method is superior to other models.
               
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