  Competitive diving is a well recognized aquatic sport in which a person dives
from a platform or a springboard into the water. Based on the acrobatics
performed during the dive, diving is classified into a finite set of action
classes which are standardized by FINA. In this work, we propose an attention
guided LSTM-based neural network architecture for the task of diving
classification. The network takes the frames of a diving video as input and
determines its class. We evaluate the performance of the proposed model on a
recently introduced competitive diving dataset, Diving48. It contains over
18000 video clips which covers 48 classes of diving. The proposed model
outperforms the classification accuracy of the state-of-the-art models in both
2D and 3D frameworks by 11.54% and 4.24%, respectively. We show that the
network is able to localize the diver in the video frames during the dive
without being trained with such a supervision.
