SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021
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Abstract (arXiv)
This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021. To participate in the challenge we deployed spatio-temporal feature extraction and aggregation models we have developed recently: GSF and XViT. GSF is an efficient spatio-temporal feature extracting module that can be plugged into 2D CNNs for video action recognition. XViT is a convolution free video feature extractor based on transformer architecture. We design an ensemble of GSF and XViT model families with different backbones and pretraining to generate the prediction scores. Our submission, visible on the public leaderboard, achieved a top-1 action recognition accuracy of 44.82%, using only RGB.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 6 Oct 2021.
Builds on
3 of 4 references.
References · 3
- 2017Temporal Segment Networks for Action Recognition in Videoscited by 279
- 2021Space-time Mixing Attention for Video Transformercited by 84
- 2019Gate-Shift Networks for Video Action Recognitioncited by 76
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.