Zero Shot Hashing

TL;DR

This paper introduces a zero-shot hashing framework that generates hash codes for images of unseen classes using auxiliary textual information, enabling recognition without prior class-specific training data.

Contribution

It proposes an unsupervised hashing method leveraging auxiliary textual signatures to predict labels for unseen classes in image recognition tasks.

Findings

Effective hash codes for unseen classes

High precision in predicting labels of unseen classes

Unsupervised approach suitable for large-scale data

Abstract

This paper provides a framework to hash images containing instances of unknown object classes. In many object recognition problems, we might have access to huge amount of data. It may so happen that even this huge data doesn't cover the objects belonging to classes that we see in our day to day life. Zero shot learning exploits auxiliary information (also called as signatures) in order to predict the labels corresponding to unknown classes. In this work, we attempt to generate the hash codes for images belonging to unseen classes, information of which is available only through the textual corpus. We formulate this as an unsupervised hashing formulation as the exact labels are not available for the instances of unseen classes. We show that the proposed solution is able to generate hash codes which can predict labels corresponding to unseen classes with appreciably good precision.