• ViT-ZSL
• • Abstract
  • Usage:
  • • • 1) Download the datasets
      • 2) Create a conda environment:
      • 3) Required libraries :
      • 4) Train (and test) the model
  • External sources:
  • Further questions:
  • Citation:
  • Authors

ViT-ZSL

PyTorch | Arxiv

PyTorch implementation of our ViT-ZSL model for zero-shot learning:
Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning
Faisal Alamri, Anjan Dutta
IMVIP, 2021

Abstract

Zero-Shot Learning (ZSL) aims to recognise unseen object classes, which are not observed during the training phase. The existing body of works on ZSL mostly relies on pretrained visual features and lacks the explicit attribute localisation mechanism on images. In this work, we propose an attention-based model in the problem settings of ZSL to learn attributes useful for unseen class recognition. Our method uses an attention mechanism adapted from Vision Transformer to capture and learn discriminative attributes by splitting images into small patches. We conduct experiments on three popular ZSL benchmarks (i.e., AWA2, CUB and SUN) and set new state-of-the-art harmonic mean results on all the three datasets, which illustrate the effectiveness of our proposed method.

Usage:

1) Download the datasets

Follow the instructions provided in data/Dataset_Instruction.txt

2) Create a conda environment:

Refer to: Conda Environment for more information.

# conda create -n {ENVNAME} python=3.6
conda create -n ViT_ZSL python=3.6

# Activate the environment: conda activate {ENVNAME}
conda activate ViT_ZSL

3) Required libraries :

This is a PyTorch implementation

pip install -r requirements.txt 

# PyTorch
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia

4) Train (and test) the model

open ViT_ZSL.ipynb

jupyter notebook ViT_ZSL.ipynb

External sources:

• Timm
• Transformer
• ViT
• Vision_Transformer_Tutorial

Further questions:

Please do read our paper.
If you still require any further information, feel free to contact us at our emails.

Citation:

If you use ViT-ZSL in your research, please use the following BibTeX entry.

@InProceedings{Alamri2021ViTZSL,
  author    = {Faisal Alamri and Anjan Dutta},
  title     = {Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning},
  booktitle = {IMVIP},
  year      = {2021}
}

Authors

• Faisal Alamri (@FaisalAlamri)
• Anjan Dutta (@AnjanDutta)