Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution
Authors: Qi Tang, Yao Zhao, Meiqin Liu, Chao Yao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments confirm our framework s advantage over state-of-the-art diffusion-based VSR techniques. |
| Researcher Affiliation | Academia | Qi Tang1,2, Yao Zhao1,2, Meiqin Liu1,2 , Chao Yao3 1 Institute of Information Science, Beijing Jiaotong University 2 Visual Intelligence + X International Cooperation Joint Laboratory of MOE, Beijing Jiaotong University 3 School of Computer and Communication Engineering, University of Science and Technology Beijing EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Generation Process of Slee Cear |
| Open Source Code | Yes | The code is available: https://github.com/Tang1705/See Clear-Neur IPS24. |
| Open Datasets | Yes | To assess the effectiveness of the proposed See Clear, we employ two commonly used datasets for training: REDS [24] and Vimeo-90K [41]. |
| Dataset Splits | Yes | In accordance with the conventions established in previous works [1, 3], we select four clips2 from the training dataset to serve as a validation dataset, referred to as REDS4. |
| Hardware Specification | Yes | The See Clear framework is implemented with Py Torch-2.0 and trained across 4 NVIDIA 4090 GPUs, each accommodating 4 video clips. |
| Software Dependencies | Yes | The See Clear framework is implemented with Py Torch-2.0 |
| Experiment Setup | Yes | All training stages utilize the Adam optimizer with β1 = 0.5 and β2 = 0.999, where the learning rate decays with the cosine annealing scheme. The Charbonnier loss [4] is applied on the whole frames between the ground truth and the reconstructed frame, formulated as L = p ||IHR i ISR i ||2 + ϵ2. |