Code for LOVEU Challenge 2022 (Track 2 Generic Event Boundary Captioning Challenge). Our model directly takes the whole video clip as input and generates a caption for each time boundary parallelly. With this design, the model could learn the context information of each time boundary, thus, the potential boundary-boundary interaction could be modeled.

Our method achieves a 72.84 score on the test set, and we reach the $2^{nd}$ place in this challenge. The technical report is available here.

Our code is adapted from the official implementation of PDVC, please see the original repo for the environment preparation.

Using CLIP to extract frame-level features and Omnivore to extract clip-level features. We use this pipeline to extract features.

Then, put the extracted features under these two folders:

data/gebc/features/clip_gebc,
data/gebc/omni_gebc

You can also directly download the official provided features here. But, remember to change the visual_feature_folder and feature_dim in the config file.

Using VinVL to extract region-level features. The region feature of a video is saved to multiple .npy files, where each single file contains the region feature of a sampled frame. Merge the feature file paths into video_to_frame_index.json in the following format:

{
    "video_id": [
        "frame_1_feat.npy",
        "frame_2_feat.npy",
        ...     
    ],
    ...
}

Then put this file under data/gebc/.

python train.py --cfg_path ${CONFIG_PATH} --gpu_id ${GPU_ID}

python eval.py --eval_folder ${EVAL_FOLDER} \
 --gpu_id=${GPU_ID} \
 --eval_caption_file=${VAL_ANNO_FILE} \
 --eval_model_path=save/${eval_folder}/model-best-dvc.pth \
 --eval_transformer_input_type gt_proposals \
 --eval_tool_version 2018_cider \
 --eval_batch_size ${EVAL_BATCHSIZE}

We train three models to predict subject, before and after, the corresponding config file and validation file are listed below:

This repo is mainly based on PDVC. We thank the authors for their efforts.