Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos
Authors: Shankhanil Mitra, Rajiv Soundararajan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several experiments on multiple cross and intra datasets to validate the performance of our proposed framework. In this section, we describe the implementation details, experimental setup, and comparisons with other methods. |
| Researcher Affiliation | Academia | Visual Information Processing Lab, Indian Institute of Science, Bengaluru {shankhanilm, rajivs}@iisc.ac.in |
| Pseudocode | No | The paper describes algorithms and processes in narrative text and with the aid of figures, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source codes and checkpoints are available at https://github.com/Shankhanil006/SSL-VQA. |
| Open Datasets | Yes | We learn our self-supervised ST-VQRL model on a set of synthetically distorted UGC videos. We randomly sample 200 videos out of 28056 training videos of LIVE-FB Large-Scale Social Video Quality (LSVQ) (Ying et al. 2021) database. ... we use a set of 60 pristine videos from LIVE-VQA (Seshadrinathan et al. 2010), LIVE Mobile (Moorthy et al. 2012), CSIQ VQD (Vu and Chandler 2014), EPFL-Po Li MI (De Simone et al. 2010) and ECCVEVVQ databases (Rimac-Drıje, Vranje, and ˇZagar 2010). ... We evaluate SSL-VQA by finetuning it for specific VQA tasks. In general, we adapt our model on four smaller VQA datasets viz. Ko NVid-1k (Hosu et al. 2017), LIVE VQC (Sinno and Bovik 2019), You Tube-UGC (Wang, Inguva, and Adsumilli 2019), and LIVE Qualcomm (Ghadiyaram et al. 2018). |
| Dataset Splits | No | The paper mentions training on a subset of LSVQ and using other datasets for testing and finetuning, but it does not explicitly specify a separate validation dataset or split percentages for hyperparameter tuning. While it mentions 'λc, and λu are chosen to be 1 based on training loss convergence', it does not define a validation set used for this convergence monitoring. |
| Hardware Specification | Yes | SSL-VQA and all other benchmarking methods were trained in Python 3.8 using Pytorch 2.0 on a 3 24 GB NVIDIA RTX 3090 GPU. |
| Software Dependencies | Yes | SSL-VQA and all other benchmarking methods were trained in Python 3.8 using Pytorch 2.0 on a 3 24 GB NVIDIA RTX 3090 GPU. |
| Experiment Setup | Yes | We train ST-VQRL using Adam W (Loshchilov and Hutter 2019) with a learning rate of 10 4 and a weight decay of 0.05 for 30 epochs. The temperature co-efficient τ mentioned in Equation (2) is 10. In both the cases we train the model for 30 epochs using Adam W (Loshchilov and Hutter 2019) with a learning rate of 10 4 and a weight decay of 0.05. λc, and λu are chosen to be 1 based on training loss convergence. |