A new multi-label similarity semantic learning(ML-SSL) model was proposed to solve the problem of label missing in existing multi-label image classification methods, It can produce better classification results by effectively recovering missing label information in training data. The model considers the characteristics of label structure and instance features, and recovers the missing label information in training data by using the label correlation within images and the similarity between images. After label recovery, the new training set is used to train the classification model, and the model is used to predict the test set. The experimental results show that the model has better performance improvement in image classification tasks under weak labeling.
In this paper, we propose an Adaptive Inductive Network(AINet), whose contributions are mainly manifested in two aspects: First, we propose a routing process evaluation method to reduce noise interference caused by different samples and obtain an accurate representation of the sample class. The second is to introduce a memory iteration mechanism in AINet, which provides a class feature template for the sample induction process to help the model quickly determine the class representation. The experimental results show that AINet can effectively handle the few-shot relationship extraction task, and demonstrate the validity of the class feature modeling method in the few-shot relationship extraction task.
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