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Our approach is among the three best to tackle the M2CAI Workflow challenge.The latter consists in recognizing the operation phase for each frames ofendoscopic videos. In this technical report, we compare several classificationmodels and temporal smoothing methods. Our submitted solution is a fine tunedResidual Network-200 on 80% of the training set with temporal smoothing usingsimple temporal averaging of the predictions and a Hidden Markov Model modelingthe sequence.

M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification