Multi-expert human action recognition with hierarchical super-class learning

In still image human action recognition, existing studies have mainly leveraged extra information along with class labels to mitigate the lack of temporal information in still images. However, preparing additional annotations such as human and object bounding boxes is time-consuming and also prone to human errors because these annotations are prepared manually. In this paper, we propose a two-phase multi-expert classification method for human action recognition by means of super-class learning and without any extra information. Specifically, 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. In the proposed approach, 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. Moreover, the proposed method copes with long-tailed distribution, which the existing studies have not addressed in action recognition. 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.