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This repository contains the annotations used for evaluating Unsupervised Domain Adaptation on EPIC Kitchens, with individual kitchens used as seperate domains. The experimental setup can be found in the Multi-modal Domain Adaptation for Fine-Grained Action Recognition (MM-SADA) publication.

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jonmun/MM-SADA_Domain_Adaptation_Splits

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MM-SADA_Domain_Adaptation_Splits

This repository contains the annotations for the domain adaptation dataset used in the paper Multi-Modal Domain Adaptation for Fine-Grained Action Recognition.

BibTeX

If this repository was utilised, please cite:

@InProceedings{munro20multi,
author = "Munro, Jonathan and Damen, Dima",
title = "{M}ulti-modal {D}omain {A}daptation for {F}ine-grained {A}ction {R}ecognition",
booktitle = "Computer Vision and Pattern Recognition (CVPR)",
year = "2020"
}

Annotations

Three domains are defined as D1, D2 and D3 from individual kitchens in the EPIC Kitchens dataset (P08, P01 and P22 respectively).

D*_train.pkl - Contains action segments for either a labelled source or unlabelled target domain. For an unlabelled target domain only video id's and timestamps should be used.

D*_test.pkl - Contains action segments for evaluation only.

verb_class is a numeric id used as the ground truth action prediction in this work.

Each pickle file contains a pandas.DataFrame with 10 columns:

Column Name Type Example Description
uid int 12917 Unique ID of the segment.
video_id string P08_01 Video the segment is in.
narration string close fridge English description of the action provided by the participant.
start_timestamp string 00:00:07.29 Start time in HH:mm:ss.SSS of the action.
stop_timestamp string 00:00:08.95 End time in HH:mm:ss.SSS of the action.
start_frame int 437 Start frame of the action (WARNING only for RGB frames extracted as detailed in Video Information).
stop_frame int 537 End frame of the action (WARNING only for RGB frames extracted as detailed in Video Information).
participant_id string P08 ID of the participant.
verb string close Parsed verb from the narration.
verb_class int 3 Numeric ID of the parsed verb's class.

Flow modality start and stop times

Optical Flow was calcuated with a stride=2 in EPIC Kitchens, therefore the start and stop frames for the Flow modality are (start_frame/2, stop_frame/2).

Downloading Frames

download_script.sh will download the frames from the relevent participants P08, P02 and P22 into the below directory structure. Unless an argument is specfied, the directory structure will be created in "$HOME/Downloads/EPIC_KITCHENS_UDA".

~/Downloads/EPIC_KITCHENS_UDA/
└── frames_rgb_flow
    ├── rgb
    │   ├── test
    │   │   ├── D1
    │   │   │   ├── P08_10.tar
    │   │   │   ├── ...
    │   │   ├── D2
    │   │   │   ├── P01_11.tar
    │   │   │   └── ...
    │   │   └── D3
    │   │       ├── P22_01.tar
    │   │       └── ...
    │   └── train
    │       ├── D1
    │       │   └── ...
    │       ├── D2
    │       │   └── ...
    │       └── D3
    │           └── ...
    └── flow
        ├── ... same file structure as rgb

About

This repository contains the annotations used for evaluating Unsupervised Domain Adaptation on EPIC Kitchens, with individual kitchens used as seperate domains. The experimental setup can be found in the Multi-modal Domain Adaptation for Fine-Grained Action Recognition (MM-SADA) publication.

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