| --- |
| license: other |
| license_name: cogvlm2 |
| license_link: https://huggingface.co/THUDM/cogvlm2-video-llama3-chat/blob/main/LICENSE |
|
|
| language: |
| - en |
| pipeline_tag: text-generation |
| tags: |
| - chat |
| - cogvlm2 |
| - cogvlm--video |
|
|
| inference: false |
| --- |
| |
| # VisionReward-Video |
|
|
| ## 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). |
|
|
