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• W2605455247 abstract "This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. The recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be trained end-to-end. In comparison with existing architectures of LSTMs, we make two key contributions giving the name to our approach as Confidence-Energy Recurrent Network -- CERN. First, instead of using the common softmax layer for prediction, we specify a novel energy layer (EL) for estimating the energy of our predictions. Second, rather than finding the common minimum-energy class assignment, which may be numerically unstable under uncertainty, we specify that the EL additionally computes the p-values of the solutions, and in this way estimates the most confident energy minimum. The evaluation on the Collective Activity and Volleyball datasets demonstrates: (i) advantages of our two contributions relative to the common softmax and energy-minimization formulations and (ii) a superior performance relative to the state-of-the-art approaches." @default.
• W2605455247 created "2017-04-28" @default.
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• W2605455247 date "2017-04-10" @default.
• W2605455247 modified "2023-09-23" @default.
• W2605455247 title "CERN: Confidence-Energy Recurrent Network for Group Activity Recognition" @default.
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• W2605455247 doi "https://doi.org/10.48550/arxiv.1704.03058" @default.
• W2605455247 hasPublicationYear "2017" @default.
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