MSRVTT-MC
Harbor: pendingThe MSRVTT-MC (Multiple Choice) dataset is a video question-answering dataset created based on the MSR-VTT dataset.
- Samples
- N/A
- Released
- 2018
- Scores
- 0
MSRVTT Causal-Temporal Narrative
MSRVTT-CTN Dataset This dataset contains CTN annotations for the MSRVTT-CTN benchmark dataset in JSON format.
$ npm i -g lettokenburn-cli$ ltb bench info msrvtt-ctn
External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.
Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs
No sourced scores recorded yet.
Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.
This dataset contains CTN annotations for the MSRVTT-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("MSRVTT_CTN_train.json", "r") as f:
msrvtt_train_data = json.load(f)
# Access the CTN captions
for video_id, ctn_caption in msrvtt_train_data.items():
cause = ctn_caption["Cause"]
effect = ctn_caption["Effect"]
# Process the cause and effect statements as neededThe MSRVTT-CTN benchmark dataset is licensed under the Creative Commons Attribution Non Commercial No Derivatives 4.0 International (CC BY-NC-ND 4.0) license.