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As an academic researcher in object recognition, I'm starting to wonder if I can still make significant contributions in this area, given that I'm competing wit
by chriskanan 12y ago
As an academic researcher in object recognition, I'm starting to wonder if I can still make significant contributions in this area, given that I'm competing with larger teams with far greater resources at Google, Microsoft, etc. For people like me, it is probably time to pivot to new problems in machine perception; the challenge is getting funding for them.
- iandanforth 12y agoThere's a lot of room left! Can you get accuracy at 120 fps? Can you do that in an embedded system? Can you use temporal and contextual queues to build a self-supervised system? Can you handle stereo and depth? Can the system handle a wide variety of image sizes? Can it handle rotation? Can it handle obstruction? There is so so much more to vision than ImageNet! Go visit your robotics lab and they'll give you a giant laundry list of things they need but don't have.
- chriskanan 12y agoSure, I agree with you that there is more to vision than ImageNet (and many-category object recognition), and that's why I indicated I'm looking for other computer vision problems to work on that I think I can solve. Incidentally, these deep convolutional neural networks do handle rotation very well (see Table 1, Krizhevsky et al): http://arxiv.org/pdf/1301.3530.pdf http://arxiv.org/pdf/1301.3530.pdf
- p1esk 12y agoIt sounds like you're not very interested in you research. If you were, you would be excited that more people are working on it, instead of being scared of competition.