Semantics-Aware Image Aesthetics Assessment using Tag Matching and Contrastive Ranking
1School of Artificial Intelligence, Xidian University
*Corresponding author
## Introduction：
### PyTorch implementation for the [paper](https://dl.acm.org/doi/abs/10.1145/3664647.3680972)
### Model weight：[./model](https://pan.baidu.com/s/13WDBJnBgHBvXUuODI4K1mw?pwd=4rd8)
## Inference Guide：
### 1. Overview
This guide will help you get started with the TMCR inference code.
### 2. Directory Structure
```
project_root/
├── AVA/
│ └── Image/ # AVA dataset images
│ └── Label/ # AVA dataset labels
├── TM/
│ └── Attr_Tags.csv # Aesthetic attribute Tags
│ └── Attr_Tags.csv # Sementic attribute Tags
│ └── TM_AVA.py # Extract TM Features
├── CR/
│ └── CR_AVA.py # Extract CR Features (Training)
├── TMCR/
│ └── TMCR.py # Testing TMCR on AVA
```
### 3. Download Required Files
```
Swin-B Pretrained Weights: Place in ./Model/swin_b-68c6b09e.pth
TMCR Model: Place your trained model at ./Model/TMCR_AVA.pt
AVA Images: Download AVA dataset images to ./AVA/images/
```
### 4. Prepare Test Data
Your test_TM.csv should have the following format:
```
image_id,score_1,score_2,...,score_10,TM_feature
123456,10,20,30,...,50,"[0.1,0.2,0.3,...,0.9]"
Columns 1-11: Image ID and 10 aesthetic score distributions
Column 12: TM_feature as a string representation of a vector
```
### 5. Running Inference
```
python TMCR.py
```
## Citation
If you find our work is useful, pleaes cite the paper:
```bibtex
@inproceedings{yang2024semantics,
title={Semantics-Aware Image Aesthetics Assessment using Tag Matching and Contrastive Ranking},
author={Yang, Zhichao and Li, Leida and Chen, Pengfei and Wu, Jinjian and Dong, Weisheng},
booktitle={Proceedings of the 32nd ACM International Conference on Multimedia},
pages={2632--2641},
year={2024}
}
```
