4 ms·
Note that many of the errors are much more understandable if one considers that the convolutional net pooling destroys much of the spatial relations in the pict
by etiam 12y ago
Note that many of the errors are much more understandable if one considers that the convolutional net pooling destroys much of the spatial relations in the pictures.
I imagine I might make similar errors if I only got little jumbled fragments to work from.
Given those conditions, the cat "laying on a couch" or the dog "jumping to catch a frisbee" hardly even seem like errors to me.
This is going to get radically better when someone works out an efficient way to keep the spatial relations.
- iamsalman 12y agoThree components: 1. Object recognition (there's dog and frisbee in the photo) 2. Object localization (Dog and frisbee's ROI in the photo) 3. Relation estimation (Based on X factors, the dog might be chasing the frisbee). Not sure what you meant by spatial relations (localization?) but recognizing (what) and localizing (where) would be key to drawing relationships between objects. Really impressive work but definitely not a leap.
- davmre 12y agoGeoff Hinton gave a talk last week at Berkeley on exactly this problem - in pixel space, object identities are all tangled up with location/pose information in a very nonlinear way; it would be nice to find a representation that actually preserves both components while disentangling them ("equivariance") instead of just throwing away all of the spatial information ("invariance", what convnets do). He's done some work on this, a lot of which is apparently unpublished, but gave a reference to one older paper covering some of the ideas: https://www.cs.toronto.edu/~hinton/absps/transauto6.pdf https://www.cs.toronto.edu/~hinton/absps/transauto6.pdf