CNN-RNN: A Unified Framework for Multi-label Image Classification

Understand

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.

• Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results.
• These techniques, although working well, fail to explicitly exploit the label dependencies in an image.
• In this paper, we utilize recurrent neural networks (RNNs) to address this problem.