mjjung/VideoLLaMA-7B-ActivityNet-VTune

TimeChat-7B-ActivityNet-VTune Model

Model details

We trained VideoLLaMA using VTune, a developed instruction-tuning method specifically designed to account for consistency.

For the tuning, we utilized 10K training videos from ActivityNet-Captions with 205K automatically generated annotations.

Evaluation

We evaluated the model on ActivtyNet-CON and ActivtyNet-Captions.

• ActivityNet-CON

  Metric	Value
  Ground	33.0
  R-Ground	24.7 (74.8)
  S-Ground	10.0 (30.2)
  H-Verify	20.2 (61.1)
  C-Verify	17.7 (53.7)

• ActivityNet-Captions

  Metric	Value
  R@1 IoU=0.3	51.58
  R@1 IoU=0.5	34.38
  R@1 IoU=0.7	19.18
  mIoU	36.16

Paper and Code for more information: Paper, Code

Citation

If you find our research and codes useful, please consider starring our repository and citing our paper:

@article{jung2024consistency,
  title={On the Consistency of Video Large Language Models in Temporal Comprehension},
  author={Jung, Minjoon and Xiao, Junbin and Zhang, Byoung-Tak and Yao, Angela},
  journal={arXiv preprint arXiv:2411.12951},
  year={2024}
}