Skeleton-Based Human Action Recognition with Noisy Labels
Yi Xu Kunyu Peng Di Wen Ruiping Liu Junwei Zheng Yufan Chen Jiaming Zhang Alina Roitberg Kailun Yang Rainer Stiefelhagen
Understanding human actions from body poses is critical for assistive robots
sharing space with humans in order to make informed and safe decisions about
the next interaction. However, precise temporal localization and annotation of
activity sequences is time-consuming and the resulting labels are often noisy.
If not effectively addressed, label noise negatively affects the model's
training, resulting in lower recognition quality. Despite its importance,
addressing label noise for skeleton-based action recognition has been
overlooked so far. In this study, we bridge this gap by implementing a
framework that augments well-established skeleton-based human action
recognition methods with label-denoising strategies from various research areas
to serve as the initial benchmark. Observations reveal that these baselines
yield only marginal performance when dealing with sparse skeleton data.
Consequently, we introduce a novel methodology, NoiseEraSAR, which integrates
global sample selection, co-teaching, and Cross-Modal Mixture-of-Experts
(CM-MOE) strategies, aimed at mitigating the adverse impacts of label noise.
Our proposed approach demonstrates better performance on the established
benchmark, setting new state-of-the-art standards. The source code for this
study will be made accessible at https://github.com/xuyizdby/NoiseEraSAR.