This vector is used as an input to a decoder module to predict patch severity grades at a future timepoint.
Facial micro-expressions are sudden involuntary minute muscle movements which reveal true emotions that people try to conceal.
We employ a novel convolutional recurrent model architecture in the Generator that efficiently deals with the word images of arbitrary width.
Our encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation.
Staff line removal is a crucial pre-processing step in Optical Music Recognition.
In this paper, we propose a novel method that involves extraction of local and global features using CNN-LSTM framework and weighting them dynamically for script identification.