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..
Boosting Text-to-Video Generative Model with MLLMs Feedback
Authors: Xun Wu, Shaohan Huang, Guolong Wang, Jing Xiong, Furu Wei
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiments confirm the effectiveness of both VIDEOPREFER and VIDEORM, representing a significant step forward in the field. |
| Researcher Affiliation | Collaboration | Xun Wu1, Shaohan Huang1B, Guolong Wang2, Jing Xiong3, Furu Wei1 1 Microsoft Research Asia, 2 University of International Business and Economics 3 The University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 DRa FT-V: Reward Reinforcement Learning for Fine-tuning Text-to-Video Models with VIDEORM |
| Open Source Code | No | We will public our data and code upon paper acceptance, due to the management regulations of our institution. |
| Open Datasets | Yes | VIDEOPREFER, which includes 135,000 preference annotations. Utilizing this dataset, we introduce VIDEORM, the first general-purpose reward model tailored for video preference in the text-to-video domain. Our comprehensive experiments confirm the effectiveness of both VIDEOPREFER and VIDEORM, representing a significant step forward in the field. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test split percentages or sample counts for its own generated dataset (VIDEOPREFER) or for how it samples from the mixture of existing datasets for training. |
| Hardware Specification | Yes | All VIDEORM series models are trained in half-precision on 8 32GB NVIDIA V100 GPUs |
| Software Dependencies | No | The paper mentions software like PyTorch and CLIP but does not provide specific version numbers for these or any other key software components. |
| Experiment Setup | Yes | All VIDEORM series models are trained in half-precision on 8 32GB NVIDIA V100 GPUs, with a learning rate of 1e-5 and batch size of 64 in total. We set the input frames N = 8. |