Welcome to the EPIC-KITCHENS-100 Action Anticipation Challenge.
The challenge requires the anticipation of a future action from the observation of a preceding video segment. The challenge will be carried out on the EPIC-KITCHENS-100 dataset. More information on the dataset & downloads can be found at https://epic-kitchens.github.io/2020-100.
Let Ta be the "anticipation time", i.e. how far in advance to anticipate the action, and To be the "observation time", i.e. the length of the observed video segment preceding the action. Given an action video segment Ai = [tsi, tei], the goal is to predict the verb/noun/action class of Ai by observing the video segment preceding the action start time tsi by Ta, that is [tsi-(Ta+To),tsi-Ta]. The anticipation time Ta is set to Ta = 1 second for this challenge. Participants are allowed to set the observation time To to whatever they find convenient. Please keep in mind that the developed algorithms are not allowed to observe any visual content temporally located after time tsi-Ta.
EPIC-KITCHENS-100 is an unscripted egocentric action dataset collected from 45 kitchens from 4 cities across the world.
Submissions are evaluated on the test set. We report Mean Top-5 Recall (MT5R) on the following subsets of the test set:
For a definition of Top-5 Recall, see Section 3.2 of . Mean Top-5 Recall is obtained by averaging Top-5 Recall values computed for each class appearing in the test set.
To submit your results to the leaderboard you must construct a submission zip file containing a single file
test.json containing the model’s results on the test set. This file should follow format detailed in the subsequent section.
The JSON submission format is composed of a single JSON object containing entries for every action in the test set. Specifically, the JSON file should contain:
'version'property, set to
'challenge'property, which can assume the following values, depending on the challenge:
slsproperties (see the Supervision Levels Scale (SLS) page for more details):
sls_pt: SLS Pretraining level.
sls_tl: SLS Training Labels level.
sls_td: SLS Training Data level.
'results'object containing entries for every action in the test set (e.g .
'P01_101_0'is the first narration ID in the test set).
Each action segment entry is a nested object composed of two entries:
'verb', specifying the class score for every verb class and the other,
'noun' specifying the score for every noun class. Action scores are automatically computed by applying softmax to the verb and noun scores and computing the probability of each possible action.
If you wish to compute your own action scores, you can augment each segment submission with exactly 100 action scores with the key
The keys of the
action object are of the form
You can provide scores in any float format that numpy is capable of reading (i.e. you do not need to stick to 3 decimal places).
If you fail to provide your own action scores we will compute them by
p(a = (v, n)) = p(v) * p(n)
To upload your results to CodaLab you have to zip the test file into a flat zip archive (it can’t be inside a folder within the archive).
You can create a flat archive using the command providing the JSON file is in your current directory.
$ zip -j my-submission.zip test.json
Start: Aug. 5, 2020, midnight
Description: CVPR 2021 Action Anticipation Challenge
May 28, 2021, 11:59 p.m.
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