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Multi-Stage LSTM (MS-LSTM) for Action Anticipation
Multi-Stage LSTM (MS-LSTM) is a deep sequence learning architecture that leverage two types of descriptors, i.e., action-aware and context-aware features to determine the action label. To do it at early stages, MS-LSTM is trained with a novel loss function that encourages correct prediction as early as possible.
Knowledge Representation and Machine Learning
Long Short-Term Memory
Source code accompanying the publication of a conference paper at the 2017 International Conference on Computer Vision
Mohammad Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann, Basura Fernando, Lars Petersson, Lars Andersson, 2017, Encouraging LSTMs To Anticipate Actions Very Early. In Proc. 2017 Int. Conf. Comput. Vision (ICCV), IEEE
GPLv3 Licence with CSIRO Disclaimer
CSIRO (Australia), Australian Centre For Robotic Vision (Australia), Australian National University (Australia)
Aliakbarian, Mohammad Sadegh; Saleh, Fatemeh Sadat; Salzmann, Mathieu; Fernando, Basura; Petersson, Lars; Andersson, Lars (2018): Multi-Stage LSTM (MS-LSTM) for Action Anticipation. v1. CSIRO. Software Collection.
All Rights (including copyright) CSIRO 2018.
The metadata and files (if any) are available to the public.
Mohammad Sadegh Aliakbarian
Fatemeh Sadat Saleh
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