Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning
Authors: Jinsong Shi, Pan Gao, Jie Qin
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on six standard IQA datasets show that Sa TQA outperforms the state-of-the-art methods for both synthetic and authentic datasets. |
| Researcher Affiliation | Academia | Jinsong Shi, Pan Gao*, Jie Qin* College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics |
| Pseudocode | No | The paper describes methods with figures and mathematical formulas but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/I2-Multimedia-Lab/Sa TQA. |
| Open Datasets | Yes | For self-supervised pre-training, we use the KADIS (Lin, Hosu, and Saupe 2020) dataset. ... The synthetic datasets include CSIQ (Larson and Chandler 2010), TID2013 (Ponomarenko et al. 2015), and KADID10K (Lin, Hosu, and Saupe 2019), and the authentic datasets include LIVE Challenge (Ghadiyaram and Bovik 2015), KONIQ (Hosu et al. 2020), and LIVE-FB (Ying et al. 2020). |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide specific percentages or counts for training/validation/test dataset splits. |
| Hardware Specification | Yes | In this paper, all of our experiments are performed using Py Torch on a single NVIDIA Ge Force RTX3090. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | During image preprocessing, we cropped 8 random 224x224 patches from each image and randomly flipped the cropped patches horizontally. ... In the training process, we use Adam W optimizer with learning rate of 2 10 5, weight decay of 1 10 2, and learning strategy of cosine annealing, where Tmax is set to 50 and etamin is 0. Experiments are trained for 150 epochs. The loss function used is L1Loss, and the batch size is 4. |