# Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration

## TL;DR

This paper presents a three-step zero-shot image recognition method that maps semantic descriptors to image features, adapts to test data, and calibrates scores, effectively addressing hubness and bias issues.

## Contribution

It introduces a novel three-step framework combining semantic-to-image mapping, test-time domain adaptation, and scaled calibration for improved zero-shot learning.

## Key findings

- Outperforms previous methods on four benchmark datasets.
- Effective reduction of hubness and bias in ZSL.
- Component-wise analysis validates each step's contribution.

## Abstract

Zero-shot learning (ZSL) for image classification focuses on recognizing novel categories that have no labeled data available for training. The learning is generally carried out with the help of mid-level semantic descriptors associated with each class. This semantic-descriptor space is generally shared by both seen and unseen categories. However, ZSL suffers from hubness, domain discrepancy and biased-ness towards seen classes. To tackle these problems, we propose a three-step approach to zero-shot learning. Firstly, a mapping is learned from the semantic-descriptor space to the image-feature space. This mapping learns to minimize both one-to-one and pairwise distances between semantic embeddings and the image features of the corresponding classes. Secondly, we propose test-time domain adaptation to adapt the semantic embedding of the unseen classes to the test data. This is achieved by finding correspondences between the semantic descriptors and the image features. Thirdly, we propose scaled calibration on the classification scores of the seen classes. This is necessary because the ZSL model is biased towards seen classes as the unseen classes are not used in the training. Finally, to validate the proposed three-step approach, we performed experiments on four benchmark datasets where the proposed method outperformed previous results. We also studied and analyzed the performance of each component of our proposed ZSL framework.