Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification

Understand

Multi-label image classification is a fundamental but challenging task in computer vision.

• Great progress has been achieved by exploiting semantic relations between labels in recent years.
• However, conventional approaches are unable to model the underlying spatial relations between labels in multi-label images, because spatial annotations of the labels are generally not provided.
• In this paper, we propose a unified deep neural network that exploits both semantic and spatial relations between labels with only image-level supervisions.