3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition

TL;DR

The paper introduces 3Mformer, a novel multi-order multi-mode transformer that models higher-order relationships in skeletal data for action recognition, outperforming existing GCN and transformer models.

Contribution

It proposes a hypergraph-based approach with a multi-order transformer architecture that captures complex joint dependencies for improved skeletal action recognition.

Findings

Achieves state-of-the-art accuracy on benchmark datasets.

Effectively models higher-order joint dependencies.

Outperforms GCN and transformer-based methods.

Abstract

Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore the dependency between not linked body joints. We propose to form hypergraph to model hyper-edges between graph nodes (e.g., third- and fourth-order hyper-edges capture three and four nodes) which help capture higher-order motion patterns of groups of body joints. We split action sequences into temporal blocks, Higher-order Transformer (HoT) produces embeddings of each temporal block based on (i) the body joints, (ii) pairwise links of body joints and (iii) higher-order hyper-edges of skeleton body joints. We combine such HoT embeddings of hyper-edges of orders 1, ..., r by a novel Multi-order Multi-mode Transformer (3Mformer) with two modules whose order can be exchanged to achieve coupled-mode attention on coupled-mode tokens based on 'channel-temporal block', 'order-channel-body joint', 'channel-hyper-edge (any order)' and 'channel-only' pairs. The first module, called Multi-order Pooling (MP), additionally learns weighted aggregation along the hyper-edge mode, whereas the second module, Temporal block Pooling (TP), aggregates along the temporal block mode. Our end-to-end trainable network yields state-of-the-art results compared to GCN-, transformer- and hypergraph-based counterparts.