MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer

This is our implementation for the paper: MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer

Abstract

With the increasing demand for video understanding, video moment and highlight detection (MHD) has emerged as a critical research topic. MHD aims to localize all moments and predict clip-wise saliency scores simultaneously. Despite progress made by existing DETR-based methods, we observe that these methods coarsely fuse features from different modalities, which weakens the temporal intra-modal context and results in insufficient cross-modal interaction. To address this issue, we propose MH-DETR (Moment and Highlight DEtection TRansformer) tailored for MHD. Specifically, we introduce a simple yet efficient pooling operator within the uni-modal encoder to capture global intra-modal context. Moreover, to obtain temporally aligned cross-modal features, we design a plug-and-play cross-modal interaction module between the encoder and decoder, seamlessly integrating visual and textual features. Comprehensive experiments on QVHighlights, Charades-STA, Activity-Net, and TVSum datasets show that MH-DETR outperforms existing state-of-the-art methods, demonstrating its effectiveness and superiority. Our code is available at -.

Table of Contents

1. Setup
2. Usage

Setup

The released code consists of the following files.

MH-DETR
├── data
│   ├── activitynet
│   │   └── {train,val}.pkl
│   ├── charades
│   │   └── {train,val}.pkl
│   ├── tvsum
│   │   └── tvsum_{train,val}.jsonl
│   └── highlight_{train,val,test}_release.jsonl
├── features
│   ├── activitynet
│   │   └── c3d.hdf5
│   ├── charades
│   |   ├── vgg.hdf5
│   │   └── i3d.hdf5
|   ├──	clip_features
|   ├── clip_text_features
|   ├── slowfast_features
|   └── tvsum
├── mh_detr
├── standalone_eval
├── utils
├── results
├── README.md
└── ···

Dependencies

# create conda env
conda create --name mh_detr python=3.9
# activate env
conda actiavte mh_detr
# install pytorch 1.13.1
conda install pytorch=1.13.1 torchvision torchaudio -c pytorch
# install other python packages
pip install tqdm ipython easydict tensorboard tabulate scikit-learn pandas timm fvcore

Datasets

QVHighlights

Download QVHighlights annotations.

Download QVHighlights_features.tar.gz (8GB) from Moment-DETR repo, extract it under project root directory:

tar -xf path/to/moment_detr_features.tar.gz

Charades-STA

Download VGG features for Charades-STA official server.

Download I3D features (password: 1234) for Charades-STA.

ActivityNet

Download C3D features for ActivityNet.

TVSum

Download TVSum features from UMT repo.

Usage

Train on QVHighlights

bash mh_detr/scripts/train.sh

Evaluation on QVHighlights

bash mh_detr/scripts/inference.sh ${Your_Path}/MH-DETR/results/qvhighlights/model_best.ckpt val

Checkpoint download link. Please replace ${Your_Path} with your path. The result is as follows:

MR [email protected]	MR [email protected]	MR mAP Avg.	HD ($\geq$ VG) mAP	HD ($\geq$ VG) HIT@1	Params	GFLOPs
60.84	44.90	39.26	38.77	61.74	8.2M	0.34

Test on QVHighlights

bash mh_detr/scripts/inference.sh ${Your_Path}/MH-DETR/results/qvhighlights/model_best.ckpt test

Train on other datasets

bash mh_detr/scripts/train_charades.sh --dset_name ${Dataset_Name}

Please replace ${Dataset_Name} with {activitynet, charades, tvsum}.

Debug

bash mh_detr/scripts/train.sh --debug