📰 News
- [2024.11.01] 🔥 We are excited to announce the release of trace-uni, which has been enhanced by incorporating additional general video understanding data from a subset of LLaVA-Video-178k. Our results indicate that trace-uni outperforms trace in both VTG tasks and general video understanding tasks.
- [2024.10.19] 🔥 We release trace-retrieval by forcing the predicted timestamps to be align with the input frame timestamps. Results show trace-retrieval achieve better performance on dense video captioning tasks.
- [2024.10.10] 🔥 Our code and paper are released!
- [2024.10.10] 🔥 Our checkpoints are available now!
Overview
In this work
- We model the videos by a series of events, and propose causal event modeling framework to capture videos' inherent structure.
- We present a novel task-interleaved video LLM model, TRACE, tailored to implement the causal event modeling framework through the sequential encoding/decoding of timestamps, salient scores, and textual captions.
Model Zoo
| Checkpoints | Description | URL |
|---|---|---|
| Initialization | Weights initialized from VideoLLaMA2 | trace-init |
| Stage-1 | Model checkpoints trained after stage-1 | trace-stage1 |
| Stage-2 | Model checkpoints trained after stage-2 | trace |
| FT-Charades | Fine-tuned on Charades-STA dataset | trace-ft-charades |
| FT-Youcook2 | Fine-tuned on Youcook2 dataset | trace-ft-youcook2 |
| FT-QVHighlights | Fine-tuned on QVHighlights dataset | trace-ft-qvhighlights |
| TRACE-retrieval | Forcing the predicted timestamps to be align with input timestamps | trace-retrieval |
| TRACE-uni | Incorporating additional general video understanding data from a subset of LLaVA-Video-178k. | trace-uni |
Results
| Youcook2 (Zero-Shot) | CIDER | METEOR | SODA_c | F1 |
|---|---|---|---|---|
| TRACE | 8.1 | 2.8 | 2.2 | 22.4 |
| TRACE-retrieal | 8.3 | 2.9 | 2.3 | 24.1 |
| TRACE-uni | 8.6 | 2.9 | 2.3 | 22.4 |
| Charades-STA (Zero-Shot) | 0.3 | 0.5 | 0.7 | mIOU |
|---|---|---|---|---|
| TRACE | 58.6 | 40.3 | 19.4 | 38.7 |
| TRACE-retrieval | 57.9 | 37.4 | 17.3 | 37.4 |
| TRACE-uni | 63.7 | 43.7 | 21.0 | 41.5 |
| QVHighlights (Zero-Shot) | mAP | Hit@1 |
|---|---|---|
| TRACE | 26.8 | 42.7 |
| TRACE-retrieval | 27.9 | 44.3 |
| TRACE-uni | 27.5 | 43.9 |
| ActivityNet-DVC | CIDER | METEOR | SODA_c | F1 |
|---|---|---|---|---|
| TRACE | 25.9 | 6.0 | 6.4 | 39.3 |
| TRACE-retrieval | 25.7 | 5.9 | 6.5 | 40.1 |
| TRACE-uni | 29.2 | 6.9 | 6.4 | 40.4 |
| ActivityNet-MR | 0.3 | 0.5 | 0.7 | mIOU |
|---|---|---|---|---|
| TRACE | 54.0 | 37.7 | 24.0 | 39.0 |
| TRACE-retrieval | 54.4 | 39.8 | 24.9 | 40.2 |
| TRACE-uni | 53.2 | 38.2 | 24.7 | 39.4 |
| MVBench | Avg | AS | AP | AA | FA | UA | OE | OI | OS | MD | AL | ST | AC | MC | MA | SC | FP | CO | EN | ER | CI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TRACE | 48.1 | 61.2 | 56.5 | 72.5 | 46.5 | 61.0 | 48.0 | 69.5 | 40.0 | 22.0 | 31.0 | 86.5 | 37.5 | 37.0 | 51.0 | 45.0 | 40.5 | 39.0 | 31.0 | 43.5 | 44.5 |
| TRACE-uni | 53.8 | 68.1 | 58.5 | 72.5 | 41.5 | 73.5 | 55.1 | 71.5 | 40.5 | 25.0 | 53.0 | 88.5 | 63.5 | 38.5 | 51.0 | 52.5 | 49.0 | 59.5 | 33.5 | 49.5 | 32.5 |
| VideoMME (w/o subtitle) | Short | Midium | Long | Avg |
|---|---|---|---|---|
| TRACE | 49.5 | 42.5 | 39.3 | 43.8 |
| TRACE-uni | 58.2 | 48.1 | 42.3 | 49.6 |
Bibliography
If you find this repository helpful for your project, please consider citing:
@misc{guo2024tracetemporalgroundingvideo,
title={TRACE: Temporal Grounding Video LLM via Causal Event Modeling},
author={Yongxin Guo and Jingyu Liu and Mingda Li and Xiaoying Tang and Qingbin Liu and Xi Chen},
year={2024},
eprint={2410.05643},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.05643},
}