Researchers Announce Advance in Image-Recognition Software
Hi all, I'm the author on the Stanford article, so I can answer any more detailed/technical questions. The article doesn't mention this, but closely related work I've only very recently become aware of (if anyone is interested) are additionally:<p>From UofT: <a href="http://arxiv.org/pdf/1411.2539v1.pdf" rel="nofollow">http://arxiv.org/pdf/1411.2539v1.pdf</a><p>From Baidu/UCLA: <a href="http://arxiv.org/pdf/1410.1090v1.pdf" rel="nofollow">http://arxiv.org/pdf/1410.1090v1.pdf</a><p>From Google: <a href="http://googleresearch.blogspot.com/2014/11/a-picture-is-worth-thousand-coherent.html" rel="nofollow">http://googleresearch.blogspot.com/2014/11/a-picture-is-wort...</a><p>From Stanford (our work):  <a href="http://cs.stanford.edu/people/karpathy/deepimagesent/" rel="nofollow">http://cs.stanford.edu/people/karpathy/deepimagesent/</a><p>Edit: And seemingly also from Berkeley <a href="http://arxiv.org/pdf/1411.4389v1.pdf" rel="nofollow">http://arxiv.org/pdf/1411.4389v1.pdf</a><p>At least from my perspective, the main motivation behind this work is in thinking of natural language as a rich label space. As humans, we've very successfully used language for communication and knowledge representation, and I think our algorithms should do the same. This also enables much more straightforward I/O interactions between a human and machine because humans speak text fluently. They don't speak a set of arbitrarily devised and assigned labels.<p>So the hope is that in the future you can, for example, search your photos based on text queries, or ask the computer questions about images, and get answers back in natural language. There's a lot of exciting work to be done!