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}
}