Major-Subordinate-Task Learning for Image Orientation Estimation

Yilin He, Wengang Zhou, Houqiang Li

Abstract: In this work, we propose a major-subordinate-task learning framework to estimate image orientation. The involved two tasks, regression to the characteristic orientation of the image (major) and classification by visual content (subordinate), are fed with shared feature and update feature extractor collaboratively. To boost the major task, we introduce a novel module, matched gradients weight multiplier, to calculate matching degree of the two tasks and adaptively adjust feedback from the subordinate task towards the shared feature extractor accordingly. As a result, such feedback is expected to be always promotive to the major task. Experiments demonstrate the effectiveness of our proposed framework over the counterpart settings.