MSVD Causal-Temporal Narrative
This dataset contains CTN annotations for the MSVD-CTN benchmark dataset in JSON format. It has three files for the train, test, and validation splits.
For project details, visit https://narrativebridge.github.io/.
Each JSON file contains a dictionary where the keys are the video IDs and the values are the corresponding Causal-Temporal Narrative (CTN) captions. The CTN captions are represented as a dictionary with two keys: "Cause" and "Effect", containing the cause and effect statements, respectively.
Example:
{
"video_id_1": {
"Cause": "a person performed an action",
"Effect": "a specific outcome occurred"
},
"video_id_2": {
"Cause": "another cause statement",
"Effect": "another effect statement"
}
}
To load the datasets, use a JSON parsing library in your preferred programming language. For example, in Python, you can use the json
module:
import json
with open("msvd_CTN_train.json", "r") as f:
msvd_train_data = json.load(f)
# Access the CTN captions
for video_id, ctn_caption in msvd_train_data.items():
cause = ctn_caption["Cause"]
effect = ctn_caption["Effect"]
# Process the cause and effect statements as needed
The MSVD-CTN benchmark dataset is licensed under the Creative Commons Attribution Non Commercial No Derivatives 4.0 International (CC BY-NC-ND 4.0) license.
Variants: MSVD-CTN
This dataset is used in 1 benchmark:
Task | Model | Paper | Date |
---|---|---|---|
Video Captioning | CEN | NarrativeBridge: Enhancing Video Captioning with … | 2024-06-10 |
Video Captioning | GIT | GiT: Towards Generalist Vision Transformer … | 2024-03-14 |
Video Captioning | SEM-POS | SEM-POS: Grammatically and Semantically Correct … | 2023-03-26 |
Recent papers with results on this dataset: