# A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning

## TL;DR

This paper introduces a unified approach for zero-shot, generalized zero-shot, and few-shot learning using Class Adapting Principal Directions, enabling effective transfer of knowledge from seen to unseen classes and improving performance across scenarios.

## Contribution

The paper proposes a novel CAPD-based method that unifies zero-shot, generalized zero-shot, and few-shot learning, with automatic class selection and adaptability to few-shot scenarios.

## Key findings

- Consistently outperforms existing methods in zero-shot learning.
- Effectively transfers information from seen to unseen classes.
- Improves generalized zero-shot learning performance with diversity estimation.

## Abstract

Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot and few-shot learning problems. Our approach is based on a novel Class Adapting Principal Directions (CAPD) concept that allows multiple embeddings of image features into a semantic space. Given an image, our method produces one principal direction for each seen class. Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class, and opposite to the other classes. This allows efficient and class-adaptive information transfer from seen to unseen classes. In addition, we propose an automatic process for selection of the most useful seen classes for each unseen class to achieve robustness in zero-shot learning. Our method can update the unseen CAPD taking the advantages of few unseen images to work in a few-shot learning scenario. Furthermore, our method can generalize the seen CAPDs by estimating seen-unseen diversity that significantly improves the performance of generalized zero-shot learning. Our extensive evaluations demonstrate that the proposed approach consistently achieves superior performance in zero-shot, generalized zero-shot and few/one-shot learning problems.