Look Closer to See Better

image source: wikipedia

Hearing about this recent research made me feel a little dumb, and hopefully you will feel the same too. But, anyways it's quite impressive to see the advanced tasks that machines are getting capable of. What we usually hear is that even though recognizing a cat is a simple task for humans, it is quite challenging task for a machine, or let's say.. for a computer.

Then, try to recognize what's in this image? If I was given this task, I would have just said that it's a 'bird', hopefully you would too, unless you are a bird expert or enthusiast. Of course it's a bird, but what if your computer is smart enough to say that it's  a 'Laysan albatross' 😂Not feeling dumb enough yet? Seems like the computer is aware of which features in which areas of its body make it a 'Laysan albatross' too.

Even though, there exists some promising research on region detection and fine grained feature learning (E.g., find which region of this bird contain more discriminative features from other bird species and then learn those features, so that we can recognize the bird species of a new, previously unseen, image), they still have some limitations.

So this research [1] focuses on a method where the two components namely attention based region detection and fine grained feature learning strengthen or reinforce each other by giving them feedback to perform better as a whole. The first component starts by looking at the coarse grained features of a given image to identify which areas to pay more attention to. Then the second component will further analyze the fine grained details of those areas to learn what features make this area unique to this species. If the second component is struggling to make confident decisions on recognizing the bird species, then it will inform that to first model as the selected region might not be very accurate. 

More information about this research can be found here.

[1] J. Fu, H. Zheng and T. Mei, "Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 4476-4484.