A Stronger Baseline for Ego-Centric Action Detection
arXiv:2106.06942
Abstract
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop. The goal of our task is to locate the start time and the end time of the action in the long untrimmed video, and predict action category. We adopt sliding window strategy to generate proposals, which can better adapt to short-duration actions. In addition, we show that classification and proposals are conflict in the same network. The separation of the two tasks boost the detection performance with high efficiency. By simply employing these strategy, we achieved 16.10\% performance on the test set of EPIC-KITCHENS-100 Action Detection challenge using a single model, surpassing the baseline method by 11.7\% in terms of average mAP.
arXiv commentCVPRW21, EPIC-KITCHENS-100 Competition Report
References in corpus (10)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Decoupled Weight Decay Regularization
- Is Space-Time Attention All You Need for Video Understanding?
- A Short Note on the Kinetics-700-2020 Human Action Dataset
- DeepViT: Towards Deeper Vision Transformer
- Self-supervised Co-training for Video Representation Learning
- Temporal Context Aggregation Network for Temporal Action Proposal Refinement
- Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
- Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition
- Self-supervised Motion Learning from Static Images