  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.
