Kanojia, GaganGaganKanojiaKumawat, SudhakarSudhakarKumawatRaman, ShanmuganathanShanmuganathanRaman2025-08-312025-08-312019-06-01[9781728125060]10.1109/CVPRW.2019.003022-s2.0-85083301767https://d8.irins.org/handle/IITG2025/24381Competitive 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.falseConference Paperhttp://export.arxiv.org/pdf/1905.00050216075162467-2476June 2019259025381cpConference Proceeding19