Cross Modal Adaptive Few-Shot Learning Based on Task Dependence
Leichao Dai, Lin Feng, Xinglin Shang, Han Su
Abstract
Few-shot learning (FSL) is a new machine learning method that applies the prior knowledge from some different domains tasks. The existing FSL models of metric-based learning have some drawbacks, such as the extracted features cannot reflect the true data distribution and the generalization ability is weak. In order to solve the problem in the present, we developed a model named cross modal adaptive few-shot learning based on task dependence (COOPERATE for short). A feature extraction and task representation method based on task condition network and auxiliary co-training is proposed. Semantic representation is added to each task by combining both visual and textual features. The measurement scale is adjusted to change the property of parameter update of the algorithm. The experimental results show that the COOPERATE has the better performance comparing with all approaches of the monomode and modal alignment FSL.