VDC-Agent-7B
Model released with our ECCV 2026 paper VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection.
VDC-Agent-7B is a video detailed captioning model fine-tuned from Qwen2.5-VL-7B-Instruct on our preference dataset VDC-Agent-19K.
Quick Start
Usage is the same as Qwen2.5-VL. Example for detailed video captioning:
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"qwangcv/VDC-Agent-7B", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("qwangcv/VDC-Agent-7B")
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "file:///path/to/video.mp4",
},
{"type": "text", "text": "Describe the video in detail."},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
Training Data
- Base model: Qwen2.5-VL-7B-Instruct
- Preference data: VDC-Agent-19K (~19K DPO pairs for detailed video captioning)
Acknowledgement
We sincerely thank the Qwen team for releasing the excellent Qwen2.5-VL models, which make this work possible.
Citation
If you find this model useful, please consider citing our paper:
@article{vdcagent,
title={VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection},
author={Wang, Qiang and Gao, Xinyuan and He, Yuhang and Han, Jizhou and Li, Jiangyang and Dong, Songlin and Ma, Zhiheng and Gong, Yihong},
journal={arXiv preprint arXiv:2511.19436},
year={2025}
}