1st Place Solution to the EPIC-Kitchens Action Anticipation Challenge 2022
Zeyu Jiang, Changxing Ding

TL;DR
This paper presents the winning solution to the EPIC-Kitchens Action Anticipation Challenge 2022, introducing novel knowledge distillation and verb-noun relation techniques to improve action anticipation accuracy.
Contribution
It introduces two innovative methods: anticipation time knowledge distillation and a verb-noun relation module, advancing action anticipation performance.
Findings
Achieved state-of-the-art results on the EPIC-Kitchens test set.
Demonstrated effectiveness of knowledge distillation for anticipation time.
Validated the benefit of verb-noun relation modeling.
Abstract
In this report, we describe the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we develop the following two approaches. 1) Anticipation Time Knowledge Distillation using the soft labels learned by the teacher model as knowledge to guide the student network to learn the information of anticipation time; 2) Verb-Noun Relation Module for building the relationship between verbs and nouns. Our method achieves state-of-the-art results on the testing set of EPIC-Kitchens Action Anticipation Challenge 2022.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Text Readability and Simplification
MethodsKnowledge Distillation
