lighthouse

Lighthouse

Lighthouse is a user-friendly library for reproducible video moment retrieval and highlight detection (MR-HD). It supports seven models, four features (video and audio features), and six datasets for reproducible MR-HD, MR, and HD. In addition, we prepare an inference API and Gradio demo for developers to use state-of-the-art MR-HD approaches easily. Furthermore, Lighthouse supports audio moment retrieval, a task to identify relevant moments from an audio input based on a given text query.

News

• [2026/01/18] Our work "CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries " has been accepted at ICASSP 2026.
• [2025/11/20] Version 1.2 Our work "CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries" has been released. This update adds support for a new AMR dataset called CASTELLA.
• [2025/06/04] Version 1.1 has been released. It includes API changes, AMR gradio demo, and huggingface wrappers for the audio moment retrieval and clotho dataset.
• [2024/12/24] Our work "Language-based audio moment retrieval" has been accepted at ICASSP 2025.
• [2024/10/22] Version 1.0 has been released.
• [2024/10/6] Our paper has been accepted at EMNLP2024, system demonstration track.
• [2024/09/25] Our work "Language-based audio moment retrieval" has been released. Lighthouse supports AMR.
• [2024/08/22] Our demo paper is available on arXiv. Any comments are welcome: Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection.

Installation

Install ffmpeg first. If you are an Ubuntu user, run:

apt install ffmpeg

Then, install pytorch, torchvision, and torchaudio based on your GPU environments. Note that the inference API is available for CPU environments. We tested the codes on Python 3.9 and CUDA 11.8:

pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118

Finally, run to install dependency libraries:

pip install 'git+https://github.com/line/lighthouse.git'

Inference API (Available for both CPU/GPU mode)

Lighthouse supports the following inference API:

import torch
from lighthouse.models import CGDETRPredictor

# use GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"

# slowfast_path is necesary if you use clip_slowfast features
query = 'A man is speaking in front of the camera'
model = CGDETRPredictor('/path/to/weight.ckpt', device=device,
                        feature_name='clip_slowfast', slowfast_path='SLOWFAST_8x8_R50.pkl')

# encode video features
video = model.encode_video('api_example/RoripwjYFp8_60.0_210.0.mp4')

# moment retrieval & highlight detection
prediction = model.predict(query, video)
print(prediction)
"""
pred_relevant_windows: [[start, end, score], ...,]
pred_saliency_scores: [score, ...]

{'query': 'A man is speaking in front of the camera',
 'pred_relevant_windows': [[117.1296, 149.4698, 0.9993],
                           [-0.1683, 5.4323, 0.9631],
                           [13.3151, 23.42, 0.8129],
                           ...],
 'pred_saliency_scores': [-10.868017196655273,
                          -12.097496032714844,
                          -12.483806610107422,
                          ...]}
"""

Lighthouse also supports the AMR inference API:

import torch
from lighthouse.models import QDDETRPredictor

device = "cuda" if torch.cuda.is_available() else "cpu"
model = QDDETRPredictor('/path/to/weight.ckpt', device=device, feature_name='clap')

audio = model.encode_audio('api_example/1a-ODBWMUAE.wav')
query = 'Water cascades down from a waterfall.'
prediction = model.predict(query, audio)
print(prediction)

Run python api_example/demo.py (MR-HD) or python api_example/amr_demo.py (AMR) to reproduce the results. It automatically downloads pre-trained weights. If you want to use other models, download pre-trained weights. When using clip_slowfast features, it is necessary to download slowfast pre-trained weights. When using clip_slowfast_pann features, in addition to the slowfast weight, download panns weights. Run python api_example/amr_demo.py to reproduce the AMR results.

Limitation: The maximum video duration is 150s due to the current benchmark datasets. For CPU users, set feature_name='clip' because CLIP+Slowfast or CLIP+Slowfast+PANNs features are very slow without GPUs.

Gradio demo

Run python gradio_demo/demo.py. Upload the video and input text query, and click the blue button. For AMR demo, run python gradio_demo/amr_demo.py.

MR-HD demo

AMR demo

Supported models, datasets, and features

Models

Moment retrieval & highlight detection

• : Moment-DETR (Lei et al. NeurIPS21)
• : QD-DETR (Moon et al. CVPR23)
• : EaTR (Jang et al. ICCV23)
• : CG-DETR (Moon et al. arXiv24)
• : UVCOM (Xiao et al. CVPR24)
• : TR-DETR (Sun et al. AAAI24)
• : TaskWeave (Jin et al. CVPR24)
• : R2-Tuning (Liu et al. ECCV24)

Datasets

Moment retrieval & highlight detection

• : QVHighlights (Lei et al. NeurIPS21)
• : QVHighlights w/ Audio Features (Lei et al. NeurIPS21)
• : QVHighlights ASR Pretraining (Lei et al. NeurIPS21)

Moment retrieval

• : ActivityNet Captions (Krishna et al. ICCV17)
• : Charades-STA (Gao et al. ICCV17)
• : TaCoS (Regneri et al. TACL13)

Highlight detection

• : TVSum (Song et al. CVPR15)
• : YouTube Highlights (Sun et al. ECCV14)

Audio moment retrieval

• : Clotho Moment/TUT2017/UnAV100-subset (Munakata et al. arXiv24)

Features

• : ResNet+GloVe
• : CLIP
• : CLIP+Slowfast
• : CLIP+Slowfast+PANNs (Audio) for QVHighlights
• : I3D+CLIP (Text) for TVSum

Reproduce the experiments

Pre-trained weights

Pre-trained weights can be downloaded from here. Download and unzip on the home directory. AMR models trained on CASTELLA and Clotho-Moment is available in here

Datasets

Due to the copyright issue, we here distribute only feature files. Download and place them under ./features directory. To extract features from videos, we use HERO_Video_Feature_Extractor.

• QVHighlights
• Charades-STA
• ActivityNet Captions
• TACoS
• TVSum
• YouTube Highlight

For AMR, download features from here.

• Clotho Moment/TUT2017/UnAV100-subset
• CASTELLA [Mirror on HF]

The whole directory should be look like this:

lighthouse/
├── api_example
├── configs
├── data
├── features # Download the features and place them here
│   ├── ActivityNet
│   │   ├── clip
│   │   ├── clip_text
│   │   ├── resnet
│   │   └── slowfast
│   ├── Charades
│   │   ├── clip
│   │   ├── clip_text
│   │   ├── resnet
│   │   ├── slowfast
│   ├── QVHighlight
│   │   ├── clip
│   │   ├── clip_text
│   │   ├── pann
│   │   ├── resnet
│   │   └── slowfast
│   ├── tacos
│   │   ├── clip
│   │   ├── clip_text
│   │   ├── resnet
│   │   └── slowfast
│   ├── tvsum
│   │   ├── clip
│   │   ├── clip_text
│   │   ├── i3d
│   │   ├── resnet
│   │   ├── slowfast
│   ├── youtube_highlight
│   │   ├── clip
│   │   ├── clip_text
│   │   └── slowfast
│   └── clotho-moments
│       ├── clap
│       └── clap_text
├── gradio_demo
├── images
├── lighthouse
├── results # The pre-trained weights are saved in this directory
└── training

Training and evaluation

Training

The training command is:

python training/train.py --model MODEL --dataset DATASET --feature FEATURE [--resume RESUME] [--domain DOMAIN]

	Options
Model	moment_detr, qd_detr, eatr, cg_detr, uvcom, tr_detr, taskweave_mr2hd, taskweave_hd2mr
Feature	resnet_glove, clip, clip_slowfast, clip_slowfast_pann, i3d_clip, clap
Dataset	qvhighlight, qvhighlight_pretrain, activitynet, charades, tacos, tvsum, youtube_highlight, clotho-moment

(Example 1) Moment DETR w/ CLIP+Slowfast on QVHighlights:

python training/train.py --model moment_detr --dataset qvhighlight --feature clip_slowfast

(Example 2) Moment DETR w/ CLIP+Slowfast+PANNs (Audio) on QVHighlights:

python training/train.py --model moment_detr --dataset qvhighlight --feature clip_slowfast_pann

(Pre-train & Fine-tuning, QVHighlights only) Lighthouse supports pre-training. Run:

python training/train.py --model moment_detr --dataset qvhighlight_pretrain --feature clip_slowfast

Then fine-tune the model with --resume option:

python training/train.py --model moment_detr --dataset qvhighlight --feature clip_slowfast --resume results/moment_detr/qvhighlight_pretrain/clip_slowfast/best.ckpt

(TVSum and YouTube Highlight) To train models on these two datasets, you need to specify domain:

python training/train.py --model moment_detr --dataset tvsum --feature clip_slowfast --domain BK

Evaluation

The evaluation command is:

python training/evaluate.py --model MODEL --dataset DATASET --feature FEATURE --split {val,test} --model_path MODEL_PATH --eval_path EVAL_PATH [--domain DOMAIN]

(Example 1) Evaluating Moment DETR w/ CLIP+Slowfast on the QVHighlights val set:

python training/evaluate.py --model moment_detr --dataset qvhighlight --feature clip_slowfast --split val --model_path results/moment_detr/qvhighlight/clip_slowfast/best.ckpt --eval_path data/qvhighlight/highlight_val_release.jsonl

To generate submission files for QVHighlight test sets, change split into test (QVHighlights only):

python training/evaluate.py --model moment_detr --dataset qvhighlight --feature clip_slowfast --split test --model_path results/moment_detr/qvhighlight/clip_slowfast/best.ckpt --eval_path data/qvhighlight/highlight_test_release.jsonl

Then zip hl_val_submission.jsonl and hl_test_submission.jsonl, and submit it to the Codalab (QVHighlights only):

zip -r submission.zip val_submission.jsonl test_submission.jsonl

HuggingFace Wrapper

We support wrappers for HuggingFace. You can easily use models and dataset via AutoModel and huggingface_hub.

The following models and datasets are provided by the wrapper for HuggingFace.

Models

• Audio Moment DETR (Munakata et al. ICASSP2024)

Datasets

• Clotho Moment (Munakata et al. ICASSP2024)

Citation

Lighthouse

@InProceedings{taichi2024emnlp,
  author    = {Taichi Nishimura and Shota Nakada and Hokuto Munakata and Tatsuya Komatsu},
  title     = {Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection},
  booktitle = {Proceedings of The 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  year      = {2024},
}

Audio moment retrieval

@InProceedings{hokuto2025icassp,
  author    = {Hokuto Munakata and Taichi Nishimura and Shota Nakada and Tatsuya Komatsu},
  title     = {Language-based Audio Moment Retrieval},
  booktitle = {IEEE International Conference on Acoustic, Speech, and Signal Processing},
  year      = {2025},
}

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

LICENSE

Apache License 2.0

Contact

Taichi Nishimura ([email protected])