The application belongs to the technical field of
deep learning, and relates to an audio and video
parsing method based on
noise label learning, which comprises the following steps: preprocessing original audio and video to obtain a segment-level input sequence; constructing a
mutual learning noise-resistant double-flow network; training the
mutual learning noise-resistant double-flow network according to a
training set; comparing the validation set indicators of two sub-networks in the trained
mutual learning noise-resistant double-flow network, and taking the sub-network with the larger validation set indicator as an audio and video
parsing model; and
parsing through the audio and video parsing model according to a
test set to obtain a
video prediction result. The mutual learning noise-resistant double-flow network is composed of two sub-networks with the same structure but different initializations, a cross filtering mechanism is executed according to the clean masks generated by the two sub-networks during the training of the mutual learning noise-resistant double-flow network, and the dynamic confidence ratio is gradually reduced through a cosine strategy, so that the problems of high pseudo-
label noise rate and easy
overfitting noise in the existing audio and video parsing task are solved.