A Novel Baseline for Zero-shot Learning via Adversarial Visual-Semantic Embedding (BMVC 2020)

• Exploit a simple and effective baseline model for zero-shot learning.
• Perform embedding-to-image generation which visually exhibits the embeddings.
• Obtain consistent and promising improvements over previous baseline models.

Dependencies

• PyTorch
• Numpy
• sklearn
• scipy

Experiment on CUB dataset

• Run python3 avse_main.py --dataset CUB --manualSeed 3483 --nclass_all 200 --lr 0.0001 --classifier_lr 0.001 --gamma 0.1 --nepoch 100 --nz 312 --attSize 312 --resSize 2048 --syn_num 300 --save_name cub

Experiment on SUN dataset

• Run python3 avse_main.py --dataset SUN --manualSeed 4115 --nclass_all 717 --lr 0.0002 --classifier_lr 0.0005 --gamma 0.1 --nepoch 100 --nz 102 --attSize 102 --resSize 2048 --syn_num 300 --save_name sun

Experiment on AWA2 dataset

• Run python3 avse_main.py --dataset AWA2 --manualSeed 9182 --nclass_all 50 --lr 0.00001 --classifier_lr 0.001 --gamma 0.1 --nepoch 50 --nz 85 --attSize 85 --resSize 2048 --syn_num 300 --save_name awa2

Experiment on Flower dataset

• Run python3 avse_main.py --dataset FLO --manualSeed 806 --nclass_all 102 --lr 0.0001 --classifier_lr 0.001 --gamma 0.1 --nepoch 100 --nz 1024 --attSize 1024 --resSize 2048 --syn_num 300 --save_name flower

Notes

• This repo is based on the codebase of f-CLSWGAN
• More instructions will be provided later.

Citation

Please cite the following paper if it is helpful for your research:

@InProceedings{AVSE_BMVC2020,
author = {Liu, Yu and Tuytelaars, Tinne}
title = {A Novel Baseline for Zero-shot Learning via Adversarial Visual-Semantic Embedding},
booktitle = {British Machine Vision Conference (BMVC)},
year = {2020}
}