metadata
base_model: Qwen/Qwen3-VL-8B-Instruct
language:
- en
license: mit
pipeline_tag: video-text-to-text
library_name: transformers
arxiv: 2602.02444
tags:
- video
- retrieval
- reranking
- qwen3-vl
datasets:
- hltcoe/RankVideo-Dataset
RankVideo
RankVideo is a video-native reasoning reranker for text-to-video retrieval, fine-tuned from Qwen3-VL-8B-Instruct.
The model explicitly reasons over query-video pairs using video content to assess relevance. It was introduced in the paper RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval.
- Repository: https://github.com/tskow99/RANKVIDEO-Reasoning-Reranker
- Paper: RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval
Training Data
This model was trained using the MultiVENT 2.0 dataset and RankVideo-Dataset.
Usage
You can use the model for scoring query-video pairs via the rankvideo library as follows:
from rankvideo import VLMReranker
reranker = VLMReranker(model_path="hltcoe/RankVideo")
# Score query-video pairs for relevance
scores = reranker.score_batch(
queries=["person playing guitar"],
video_paths=["/path/to/video.mp4"],
)
print(f"Relevance score: {scores[0]['logit_delta_yes_minus_no']:.3f}")
BibTeX
@misc{skow2026rankvideoreasoningrerankingtexttovideo,
title={RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval},
author={Tyler Skow and Alexander Martin and Benjamin Van Durme and Rama Chellappa and Reno Kriz},
year={2026},
eprint={2602.02444},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2602.02444},
}