Awesome

Continual Zero-Shot Learning through Semantically Guided Generative Random Walks

This is the official PyTorch implementation for our ICCV-2023 paper

Continual Zero-Shot Learning through Semantically Guided Generative Random Walks
Wenxuan Zhang, Paul Janson, Kai Yi, Ivan Skorokhodov, and Mohamed Elhoseiny
<a href='https://arxiv.org/abs/2308.12366'><img src='https://img.shields.io/badge/arXiv-2308.12366-red'></a>

Update

We updated the code to be compatible with PyTorch>=1.5 in this branch

Introduction

In this project, we explore the problem of inductive continual zero-shot learning. Here, our approach stands out by not relying on any previously unseen information during training,but hallucinated (synthesized) feature instead. Through comprehensive theoretical analysis, we discover that the key lies in the density and diversity of these hallucinated examples within the feature space.

To achieve this objective, we introduce two attribute generation techniques: interpolation and learnable dictionary. These methods work in tandem to foster a densely populated representation of the hallucinated features. Furthermore, we present an innovative Generative Random Walk (GRW) loss, guided by semantic information. This loss function is designed to enhance the diversity of the synthesized features. These allowed us to outperform both the existing transductive and inductive continual zero-shot learning methods.

We mainly follow the CZSL setting of this paper. Our backbone highly depends on this paper.

Download the dataset

Run the following script to download the dataset

wget http://datasets.d2.mpi-inf.mpg.de/xian/xlsa17.zip --no-check-certificate -O xlsa17.zip
unzip xlsa17
mkdir data
mv xlsa17/data/* data/
rm -rf xlsa17.zip xlsa17

Environment preparation

Create and activate conda environment

conda env create --file environment.yaml --prefix ./env
conda activate ./env

Running experiments

DATASET=[AWA1|AWA2|CUB|SUN]
METHOD=[learnable|interpolation]
python main.py \
  --dataset $DATASET \
  --attribute_generation_method $METHOD \
  --batch_size 128 \
  --buffer_size 5000  \
  --load_best_hp

Results

Citation

@inproceedings{cgzsl,
  author    = {Wenxuan Zhang and Paul Janson and Kai Yi and Ivan Skorokhodov and Mohamed Elhoseiny},
  title     = {Continual Zero-Shot Learning through Semantically Guided Generative Random Walks},
  booktitle   = {IEEE/CVF International Conference on Computer Vision},
  year      = {2023},
}