Cross Modal Adaptive Few-Shot Learning Based on Task Dependence  

作　　者：DAI Leichao FENG Lin SHANG Xinglin SU Han 

机构地区：[1]School of Computer Science,Sichuan Normal University,Chengdu 610101,China

出　　处：《Chinese Journal of Electronics》2023年第1期85-96,共12页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (61876158);Fundamental Research Funds for the Central Universities (2682021ZTPY030)。

摘　　要：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.

关 键 词：META-LEARNING Few-shot learning Metric learning Cross modal 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]