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---
language:
- en
license: other
license_name: cogvlm2
license_link: https://huggingface.co/THUDM/cogvlm2-video-llama3-chat/blob/main/LICENSE
pipeline_tag: feature-extraction
library_name: transformers
tags:
- chat
- cogvlm2
- cogvlm--video
inference: false
---
# VisionReward-Video
This repository contains the model described in the paper [VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation](https://huggingface.co/papers/2412.21059).
## Introduction
We present VisionReward, a general strategy to aligning visual generation models——both image and video generation——with human preferences through a fine-grainedand multi-dimensional framework. We decompose human preferences in images and videos into multiple dimensions,each represented by a series of judgment questions, linearly weighted and summed to an interpretable and accuratescore. To address the challenges of video quality assess-ment, we systematically analyze various dynamic features of videos, which helps VisionReward surpass VideoScore by 17.2% and achieve top performance for video preference prediction.
Here, we present the model of VisionReward-Video.
## Using this model
You can quickly install the Python package dependencies and run model inference in our [github](https://github.com/THUDM/VisionReward). |