Codes for the AAAI'22 paper "TransZero: Attribute-guided Transformer for Zero-Shot Learning"

Overview

TransZero [arXiv]

This repository contains the testing code for the paper "TransZero: Attribute-guided Transformer for Zero-Shot Learning" accepted to AAAI 2022. 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
│   ├── TransZero_CUB_CZSL.pth
│   ├── TransZero_CUB_GZSL.pth
│   ├── TransZero_SUN_CZSL.pth
│   ├── TransZero_SUN_GZSL.pth
│   ├── TransZero_AWA2_CZSL.pth
│   └── TransZero_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 76.8 69.3 68.3 68.8
SUN 65.6 52.6 33.4 40.8
AWA2 70.1 61.3 82.3 70.2

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

Citation

If this work is helpful for you, please cite our paper.

@InProceedings{Chen2021TransZero,
    author    = {Chen, Shiming and Hong, Ziming and Liu, Yang and Xie, Guo-Sen and Sun, Baigui and Li, Hao and Peng, Qinmu and Lu, Ke and You, Xinge},
    title     = {TransZero: Attribute-guided Transformer for Zero-Shot Learning},
    booktitle = {Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI)},
    year      = {2022}
}

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
DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control

DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control One version of our system is implemented using the

260 Nov 28, 2022
Testing the Facial Emotion Recognition (FER) algorithm on animations

PegHeads-Tutorial-3 Testing the Facial Emotion Recognition (FER) algorithm on animations

PegHeads Inc 2 Jan 03, 2022
tmm_fast is a lightweight package to speed up optical planar multilayer thin-film device computation.

tmm_fast tmm_fast or transfer-matrix-method_fast is a lightweight package to speed up optical planar multilayer thin-film device computation. It is es

26 Dec 11, 2022
Motion Reconstruction Code and Data for Skills from Videos (SFV)

Motion Reconstruction Code and Data for Skills from Videos (SFV) This repo contains the data and the code for motion reconstruction component of the S

268 Dec 01, 2022
Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018

Adversarial Learning for Semi-supervised Semantic Segmentation This repo is the pytorch implementation of the following paper: Adversarial Learning fo

Wayne Hung 464 Dec 19, 2022
A PyTorch implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

Caiyong Wang 14 Sep 20, 2022
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

HV-plane reconstruction from a single 360 image Code for our paper in CVPR 2021: Indoor Panorama Planar 3D Reconstruction via Divide and Conquer (pape

sunset 36 Jan 03, 2023
Udacity Suse Cloud Native Foundations Scholarship Course Walkthrough

SUSE Cloud Native Foundations Scholarship Udacity is collaborating with SUSE, a global leader in true open source solutions, to empower developers and

Shivansh Srivastava 34 Oct 18, 2022
Code for paper "Context-self contrastive pretraining for crop type semantic segmentation"

Code for paper "Context-self contrastive pretraining for crop type semantic segmentation" Setting up a python environment Follow the instruction in ht

Michael Tarasiou 11 Oct 09, 2022
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress

Jamie Simon 45 Dec 02, 2022
PG2Net: Personalized and Group PreferenceGuided Network for Next Place Prediction

PG2Net PG2Net:Personalized and Group Preference Guided Network for Next Place Prediction Datasets Experiment results on two Foursquare check-in datase

Urban Mobility 5 Dec 20, 2022
Official implementation of Deep Burst Super-Resolution

Deep-Burst-SR Official implementation of Deep Burst Super-Resolution Publication: Deep Burst Super-Resolution. Goutam Bhat, Martin Danelljan, Luc Van

Goutam Bhat 113 Dec 19, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
Convert Python 3 code to CUDA code.

Py2CUDA Convert python code to CUDA. Usage To convert a python file say named py_file.py to CUDA, run python generate_cuda.py --file py_file.py --arch

Yuval Rosen 3 Jul 14, 2021
A Re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"

What is This This is a simple re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"(1). Only Sections

102 Dec 14, 2022
Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Mingrui Yu 3 Jan 07, 2022
A comprehensive list of published machine learning applications to cosmology

ml-in-cosmology This github attempts to maintain a comprehensive list of published machine learning applications to cosmology, organized by subject ma

George Stein 290 Dec 29, 2022
Alternatives to Deep Neural Networks for Function Approximations in Finance

Alternatives to Deep Neural Networks for Function Approximations in Finance Code companion repo Overview This is a repository of Python code to go wit

15 Dec 17, 2022
A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

python_graphs This package is for computing graph representations of Python programs for machine learning applications. It includes the following modu

Google Research 258 Dec 29, 2022
PyTorch implementation for the Neuro-Symbolic Sudoku Solver leveraging the power of Neural Logic Machines (NLM)

Neuro-Symbolic Sudoku Solver PyTorch implementation for the Neuro-Symbolic Sudoku Solver leveraging the power of Neural Logic Machines (NLM). Please n

Ashutosh Hathidara 60 Dec 10, 2022