Title: DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING. Abstract: Object recognition in real-world image scenes is still an open problem. With the growing number of classes, the similarity structures between them become complex and the distinction between classes blurs, which makes the classification problem particularly challenging. Standard N-way discrete classifiers treat all classes as disconnected and unrelated, and therefore unable to learn from their semantic relationships. In this work, we present a hierarchical inter-class relationship model and train it using a newly proposed probability-based loss function. Our hierarchical model provides significantly better semantic generalization ability compared to a regular N-way classifier. We further proposed an algorithm where given a probabilistic classification model it can return the input corresponding super-group based on classes hierarchy without any further learning. We deploy it in two scenarios in which super-group retrieval can be useful. The first one, selective classification, deals with the problem of low-confidence classification, wherein a model is unable to make a successful exact classification. 
The second, zero-shot learning problem deals with making reasonable inferences on novel classes. Extensive experiments with the two scenarios show that our proposed hierarchical model yields more accurate and meaningful super-class predictions compared to a regular N-way classifier because of its significantly better semantic generalization ability.