Free-Form Composition Networks for Egocentric Action Recognition

TL;DR

This paper introduces a free-form composition network (FFCN) that learns disentangled verb, preposition, and noun representations to generate new samples for rare egocentric actions, improving recognition performance in data-scarce scenarios.

Contribution

The paper proposes a novel FFCN that decomposes actions into spatial-temporal verb and preposition representations and composes new samples, addressing data scarcity in egocentric action recognition.

Findings

Significantly improves recognition of rare classes.

Effective on multiple egocentric datasets.

Addresses long-tailed and few-shot learning challenges.

Abstract

Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognition from a compositional generalization perspective. To tackle this problem, we propose a free-form composition network (FFCN) that can simultaneously learn disentangled verb, preposition, and noun representations, and then use them to compose new samples in the feature space for rare classes of action videos. First, we use a graph to capture the spatial-temporal relations among different hand/object instances in each action video. We thus decompose each action into a set of verb and preposition spatial-temporal representations using the edge features in the graph. The temporal decomposition extracts verb and preposition representations from different video frames, while the spatial decomposition adaptively learns verb and preposition representations from action-related instances in each frame. With these spatial-temporal representations of verbs and prepositions, we can compose new samples for those rare classes in a free-form manner, which is not restricted to a rigid form of a verb and a noun. The proposed FFCN can directly generate new training data samples for rare classes, hence significantly improve action recognition performance. We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition.