In order to handle this problem, in this paper, we propose an LSTM-based model that can fully consider the relations among the utterances and also handle the multi-modal feature fusion problem in the learning process. Finally, the experiments on some popular datasets demonstrate the effectiveness...
–spectral features from homogeneous or heterogeneous VHR image patches. The MRNN stacked by long-short term memory (LSTM) units is responsible for mapping the spatial–spectral features extracted by DSCNN into a new latent feature space and mining the change information between them. In addition,...
For example, the bidirectional LSTM layer has 6 weights by default, and the first 3s belong to the forward layer. The 2nd weight (recurrent kernel) in the forward layer controls the computation of gates for recurrent connections. The kernel for computing cell states lays in units x 2 to uni...
Multi-modal feature fusion based on multi-layers LSTM for video emotion recognitiondoi:10.1007/s11042-020-08796-8Weizhi NieYan YanDan SongKun WangSpringer US
LSTMDriver-drowsinessRecent developments in the automotive industry have led to an interest in monitoring car driver drowsiness. The purpose is to develop an efficient system for the detection of bad psychophysical states in order to reduce the number of fatigue-related car accidents. Much of the ...