5,645 research outputs found
CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection
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.Comment: technical repor
- …