CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection

TL;DR

CycleACR introduces a bidirectional actor-context relation modeling approach that selectively leverages scene context to enhance video action detection, achieving state-of-the-art results on AVA and UCF101-24 datasets.

Contribution

The paper proposes a novel symmetric graph model with actor-to-context reorganization and context-to-actor enhancement for improved relation modeling in action detection.

Findings

Achieves state-of-the-art performance on AVA and UCF101-24 datasets.

Demonstrates the effectiveness of bidirectional relation modeling.

Provides ablation studies and visualizations supporting the approach.

Abstract

The relation modeling between actors and scene context advances video action detection where the correlation of multiple actors makes their action recognition challenging. Existing studies model each actor and scene relation to improve action recognition. However, the scene variations and background interference limit the effectiveness of this relation modeling. In this paper, we propose to select actor-related scene context, rather than directly leverage raw video scenario, to improve relation modeling. We develop a Cycle Actor-Context Relation network (CycleACR) where there is a symmetric graph that models the actor and context relations in a bidirectional form. Our CycleACR consists of the Actor-to-Context Reorganization (A2C-R) that collects actor features for context feature reorganizations, and the Context-to-Actor Enhancement (C2A-E) that dynamically utilizes reorganized context features for actor feature enhancement. Compared to existing designs that focus on C2A-E, our CycleACR introduces A2C-R for a more effective relation modeling. This modeling advances our CycleACR to achieve state-of-the-art performance on two popular action detection datasets (i.e., AVA and UCF101-24). We also provide ablation studies and visualizations as well to show how our cycle actor-context relation modeling improves video action detection. Code is available at https://github.com/MCG-NJU/CycleACR.