Multi-Expert Human Action Recognition with Hierarchical Super-Class Learning

TL;DR

This paper introduces a two-phase multi-expert classification approach for human action recognition in still images, effectively handling long-tailed data distributions without extra annotations, and demonstrates superior performance across multiple datasets.

Contribution

The paper presents a novel super-class learning framework with a Graph-Based Class Selection algorithm, improving action recognition accuracy without additional data annotation.

Findings

Outperforms state-of-the-art methods on multiple datasets.

Effectively handles long-tailed class distributions.

Reduces computational cost compared to existing approaches.

Abstract

In still image human action recognition, existing studies have mainly leveraged extra bounding box information along with class labels to mitigate the lack of temporal information in still images; however, preparing extra data with manual annotation is time-consuming and also prone to human errors. Moreover, the existing studies have not addressed action recognition with long-tailed distribution. In this paper, we propose a two-phase multi-expert classification method for human action recognition to cope with long-tailed distribution by means of super-class learning and without any extra information. To choose the best configuration for each super-class and characterize inter-class dependency between different action classes, we propose a novel Graph-Based Class Selection (GCS) algorithm. In the proposed approach, a coarse-grained phase selects the most relevant fine-grained experts. Then, the fine-grained experts encode the intricate details within each super-class so that the inter-class variation increases. Extensive experimental evaluations are conducted on various public human action recognition datasets, including Stanford40, Pascal VOC 2012 Action, BU101+, and IHAR datasets. The experimental results demonstrate that the proposed method yields promising improvements. To be more specific, in IHAR, Sanford40, Pascal VOC 2012 Action, and BU101+ benchmarks, the proposed approach outperforms the state-of-the-art studies by 8.92%, 0.41%, 0.66%, and 2.11 % with much less computational cost and without any auxiliary annotation information. Besides, it is proven that in addressing action recognition with long-tailed distribution, the proposed method outperforms its counterparts by a significant margin.