Everyone wants to do the model work, not the data work [pdf]
Real story. I worked on nudity detection for mobile realtime video chat app with on-device deep learning model. (It was successful.) Should be easy: label some data, start from a good network pretrained on ImageNet (we used SqueezeNet), fine tune. The problem was that ImageNet is photos, not video frames, and distribution of video frames from mobile video is somewhat different. Incredibly large proportion of frames are either blank (camera is blocked) or ceiling (camera is misdirected). We ended up adding lots of data for blank and ceiling explicitly labeled as blank and ceiling. It became an excellent ceiling detector, detecting ceilings it never have seen before.<p>Is there a mobile video frames dataset for labeling ceilings? Why not? I can't believe I am the only one who experienced this. Why is not a ceiling dataset worthy of, say, CVPR? It will improve mobile video on-device deep learning more than most of CVPR papers. This is a serious problem.<p>Edit: I understand why niche datasets remain in industry and not academia. Public datasets are better if they are of general interest. But ceiling dataset is of general interest to anyone who wants to process video frames originating from blocked or misdirected camera aka smartphones, and it's hard to imagine topics of more general interest once you have obvious things like face.