FBK-HUPBA Submission to the EPIC-Kitchens Action Recognition 2020 Challenge

• URL: http://arxiv.org/abs/2006.13725v1
• Date: Wed, 24 Jun 2020 13:41:17 GMT
• Title: FBK-HUPBA Submission to the EPIC-Kitchens Action Recognition 2020 Challenge
• Authors: Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
• Abstract summary: We describe the technical details of our submission to the EPIC-Kitchens Action Recognition 2020 Challenge. Our submission achieved top Ego-1 action recognition accuracy of 40.0% on S1 setting, and 21% on S2 setting, using only RGB.
• Score: 43.8525418821458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this report we describe the technical details of our submission to the EPIC-Kitchens Action Recognition 2020 Challenge. To participate in the challenge we deployed spatio-temporal feature extraction and aggregation models we have developed recently: Gate-Shift Module (GSM) [1] and EgoACO, an extension of Long Short-Term Attention (LSTA) [2]. We design an ensemble of GSM and EgoACO model families with different backbones and pre-training to generate the prediction scores. Our submission, visible on the public leaderboard with team name FBK-HUPBA, achieved a top-1 action recognition accuracy of 40.0% on S1 setting, and 25.71% on S2 setting, using only RGB.

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