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In this work, we propose hierarchical event memory for online video temporal grounding (OnVTG), which requires the model to locate events related to a given natural language query within a video stream. Unlike regular video temporal grounding, OnVTG requires the model to make predictions without observing future frames. We achieve efficient, accurate, and real-time online video temporal localization. We validate the effectiveness of our method on the ActivityNet Captions, TACoS, and MAD datasets.

Our paper is accepted by ICCV-2025.

Requiments

Please install the necessary dependencies listed in requirements.txt.

Data Preparation

Download the data for ActivityNet Captions and TACoS datasets from Google Drive or Baidu Netdisk. Please download the data for MAD dataset from the official repository.

Please decompress the data and place it in the data directory.

Training and Inference

ActivityNet Captions

To train the model on the ActivityNet Captions dataset:

CUDA_VISIBLE_DEVICES=0 bash scripts/train.sh configs/anet.yaml

You can download the trained model checkpoint from Google Drive or Baidu Netdisk.

To evaluate the trained model:

# Inference using single GPU
CUDA_VISIBLE_DEVICES=0 bash scripts/eval.sh configs/anet.yaml results/anet/models/best.pth
# Inference using multiple GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/eval.sh configs/anet.yaml results/anet/models/best.pth

You can replace results/anet/models/best.pth with the downloaded checkpoint.

The results should be:

Method	$R_{0.5}^1$	$R_{0.7}^1$	$R_{0.5}^5$	$R_{0.7}^5$	SD	ED
With Future Prediction	42.89	24.49	67.82	51.74	-1.58s	-10.96s
Without Future Prediction	45.29	26.25	76.24	62.14	41.10s	-10.89s

MAD

To train the model on the MAD dataset:

CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/train.sh configs/mad.yaml

If you wish to train using a different number of GPUs, please modify the batch_size in configs/mad.yaml to ensure that the batch_size multiplied by the number of GPUs remains unchanged.

You can download the trained model checkpoint from Google Drive or Baidu Netdisk.

To evaluate the trained model:

# Inference using single GPU
CUDA_VISIBLE_DEVICES=0 bash scripts/eval.sh configs/mad.yaml results/mad/models/best.pth
# Inference using multiple GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/eval.sh configs/mad.yaml results/mad/models/best.pth

Please replace results/mad/models/best.pth with the downloaded checkpoint.

The results should be:

Method	$R_{0.3}^5$	$R_{0.5}^5$	$R_{0.3}^{50}$	$R_{0.5}^{50}$	SD	ED
With Future Prediction	9.84	6.43	16.67	12.18	0.64s	-1.10s
Without Future Prediction	15.76	11.07	37.84	29.21	3.60s	-1.45s

TACoS

To train the model on the TACoS dataset:

CUDA_VISIBLE_DEVICES=0 bash scripts/train.sh configs/tacos.yaml

You can download the trained model checkpoint from Google Drive or Baidu Netdisk.

To evaluate the trained model:

# Inference using single GPU
CUDA_VISIBLE_DEVICES=0 bash scripts/eval.sh configs/tacos.yaml results/tacos/models/best.pth
# Inference using multiple GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/eval.sh configs/tacos.yaml results/tacos/models/best.pth

Please replace results/tacos/models/best.pth with the downloaded checkpoint.

The results should be:

Method	$R_{0.5}^1$	$R_{0.7}^1$	$R_{0.5}^5$	$R_{0.7}^5$	SD	ED
With Future Prediction	37.44	27.32	57.49	44.44	-1.28s	-3.78s
Without Future Prediction	44.19	30.87	68.96	52.69	22.38s	-4.26s

Citation

@inproceedings{zheng-etal-2025-hierarchical,
    title = "Hierarchical Event Memory for Accurate and Low-latency Online Video Temporal Grounding",
    author = "Zheng, Minghang  and
      Peng, Yuxin  and
      Sun, Benyuan and
      Yang, Yi and
      Liu, Yang",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)",
    year = "2025"
}