Long-term Recurrent Convolutional Networks for Visual Recognition and Description · HackerPapers discussions

Long-term Recurrent Convolutional Networks for Visual Recognition and Description — mentioned by DeonPenny

Mon, 29 Apr 2019 22:20:50 GMT

1. You can get 89% accuracy with a convolutional LSTM https://arxiv.org/pdf/1411.4389.pdf 2. You get multiple bites of the apple the chip is processing 200 images a second. It had plenty of sign in the next frame to identify an issue. 3. All Tesla have redundant side sensors and radars, and an adversarial system for checking it's conclusions. 4. Having don't actually need to file those things in Tesla case. Why would you? They could easily test the functionality and tell no one and not worry about the regulation until they've passed a certain amount of accuracy. 5. The beta is navigation on…

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Long-term Recurrent Convolutional Networks for Visual Recognition and Description — mentioned by zan2434

Thu, 22 Jan 2015 19:10:15 GMT

>Perhaps a neural network can tell what a cat looks like, but can it tell the difference between a dead cat and a sleeping one, by watching it breathe? Yes, yes it can: http://arxiv.org/abs/1411.4389 (Long-term Recurrent Convolutional Networks for Visual Recognition and Description) It's certainly surprising how much of perception can be reduced to classification.

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Long-term Recurrent Convolutional Networks for Visual Recognition and Description — mentioned by tfgg

Tue, 18 Nov 2014 15:11:24 GMT

ArXiv abstract links, which are preferable to direct links to the PDF: http://arxiv.org/abs/1411.2539 http://arxiv.org/abs/1410.1090 http://arxiv.org/abs/1411.4389

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Long-term Recurrent Convolutional Networks for Visual Recognition and Description — mentioned by karpathy

Tue, 18 Nov 2014 01:24:44 GMT

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: From UofT: http://arxiv.org/pdf/1411.2539v1.pdf From Baidu/UCLA: http://arxiv.org/pdf/1410.1090v1.pdf From Google: http://googleresearch.blogspot.com/2014/11/a-picture-is-wort... From Stanford (our work): http://cs.stanford.edu/people/karpathy/deepimagesent/ Edit: And seemingly also from Berkeley http://arxiv.org/pdf/1411.4389v1.pdf At least from my…

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