  Blind video decaptioning is a problem of automatically removing text overlays
and inpainting the occluded parts in videos without any input masks. While
recent deep learning based inpainting methods deal with a single image and
mostly assume that the positions of the corrupted pixels are known, we aim at
automatic text removal in video sequences without mask information. In this
paper, we propose a simple yet effective framework for fast blind video
decaptioning. We construct an encoder-decoder model, where the encoder takes
multiple source frames that can provide visible pixels revealed from the scene
dynamics. These hints are aggregated and fed into the decoder. We apply a
residual connection from the input frame to the decoder output to enforce our
network to focus on the corrupted regions only. Our proposed model was ranked
in the first place in the ECCV Chalearn 2018 LAP Inpainting Competition Track2:
Video decaptioning. In addition, we further improve this strong model by
applying a recurrent feedback. The recurrent feedback not only enforces
temporal coherence but also provides strong clues on where the corrupted pixels
are. Both qualitative and quantitative experiments demonstrate that our full
model produces accurate and temporally consistent video results in real time
(50+ fps).
