TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

Overview

TransZero++

This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted to TPAMI. We will release all codes of this work later.

Preparing Dataset and Model

We provide trained models (Google Drive) on three different datasets: CUB, SUN, AWA2 in the CZSL/GZSL setting. You can download model files as well as corresponding datasets, and organize them as follows:

.
├── saved_model
│   ├── TransZeroPP_CUB_CZSL.pth
│   ├── TransZeroPP_CUB_GZSL.pth
│   ├── TransZeroPP_SUN_CZSL.pth
│   ├── TransZeroPP_SUN_GZSL.pth
│   ├── TransZeroPP_AWA2_CZSL.pth
│   └── TransZeroPP_AWA2_GZSL.pth
├── data
│   ├── CUB/
│   ├── SUN/
│   └── AWA2/
└── ···

Requirements

The code implementation of TransZero++ mainly based on PyTorch. All of our experiments run and test in Python 3.8.8. To install all required dependencies:

$ pip install -r requirements.txt

Runing

Runing following commands and testing TransZero++ on different dataset:

CUB Dataset:

$ python test.py --config config/CUB_CZSL.json      # CZSL Setting
$ python test.py --config config/CUB_GZSL.json      # GZSL Setting

SUN Dataset:

$ python test.py --config config/SUN_CZSL.json      # CZSL Setting
$ python test.py --config config/SUN_GZSL.json      # GZSL Setting

AWA2 Dataset:

$ python test.py --config config/AWA2_CZSL.json     # CZSL Setting
$ python test.py --config config/AWA2_GZSL.json     # GZSL Setting

Results

Results of our released models using various evaluation protocols on three datasets, both in the conventional ZSL (CZSL) and generalized ZSL (GZSL) settings.

Dataset Acc(CZSL) U(GZSL) S(GZSL) H(GZSL)
CUB 78.3 67.5 73.6 70.4
SUN 67.6 48.6 37.8 42.5
AWA2 72.6 64.6 82.7 72.5

Note: All of above results are run on a server with an AMD Ryzen 7 5800X CPU and a NVIDIA RTX A6000 GPU.

References

Parts of our codes based on:

Contact

If you have any questions about codes, please don't hesitate to contact us by [email protected] or [email protected].

Owner
Shiming Chen
Interest: Generative modeling and learning, zero-shot learning, image retrieval, domain adaptation
Shiming Chen
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