Data annotation

[LiuRicky]

Thanks for your great work.

About the Intent-QA pairs. It seems it is a filter process based on NeXT-QA, and no more new annotation is added. Am I understanding this correctly?

So what is the meaning of "annotation" in "After filtering and annotation, our IntentQA dataset ..." (Sec. 3, Data Statistics, first sentence)? Are there any new annotation added? I might miss something important.

[JoseponLee]

Sorry for the late reply. We have updated the README and provided detailed descriptions of the additional annotations in the Dataset section. You can download the annotation file, pre-extracted features, original videos, and model weights by clicking on the links provided.

Dataset

Please download the pre-computed features and original videos from here,

There are 3 folders:

• Videos: This directory contains all the original videos of the dataset, named with video_id. All videos are in MP4 format.
• region_feat_n: This folder contains pre-computed bounding box features.
• frame_feat: This folder includes pre-computed frame features.

Please download the QA annotations from here. There are 3 files (train.csv,val.csv,test.csv ):

In each annotation file, the initial columns follow the same format as in NExT-QA. Building upon the NExT-QA foundation, we have introduced additional annotations, adding extra columns to the dataset.

• action, lemma, and lemma_id: Specifically, we have annotated action, lemma, and lemma_id. These columns highlight actions in the current QA that trigger intentions, either self or others', along with the lemmatized forms of these actions and their corresponding IDs after categorizing them into synonymous groups.

• id, pos_id, and neg_id: Furthermore, in the train.csv file, we have also added id, pos_id, and neg_id annotations. The id column denotes the row number of the data, while the pos_id and neg_id columns indicate the row numbers (id) of data in the train set that form positive and negative cases, respectively, in relation to the current row's data.