Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks · HackerPapers discussions

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks — mentioned by airgapstopgap

Mon, 28 Nov 2022 03:45:10 GMT

This is such a brazen deflection I had to create an account to point it out. Racism, seriously? No, papers that advance the SOTA, and more importantly introduce powerful novel concepts and techniques, have plenty of ethnic Chinese authors. The Chinese are very numerous in the field, just like in software in general, unsurprisingly. It's just those authors overwhelmingly work in the United States, and rarely in other allied countries. Perhaps the highest-cited ML paper that can be attributed to Mainland China is still affiliated with Microsoft [1]. The fearmongering is miscalibrated, whether…

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks — mentioned by joshvm

Sun, 12 Nov 2017 16:53:14 GMT

Absolutely you can do 'AI Research' without a degree in maths. Sure you need a grounding in linear algebra, stats, probability and calculus, but not much more than a CS or physics degree will teach you. That stuff is indeed learned easily, and it's not what the Reddit user is worried about. That also ignores applications of machine learning, which is also a massive (and lucrative) field. But because it's a trendy field, I think there is an obsession with people needing to understand everything theoretical that comes out for fear of missing the boat. Some of the really interesting papers that…

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks — mentioned by edshiro

Wed, 08 Nov 2017 11:30:26 GMT

There are quite a few papers that discuss the application R-CNN (Region-based Convolutional Neural Network) for object detection and classification [1-3]. On websites such as Medium, you can find lots of blog posts on object detection using popular frameworks such as Tensorflow (e.g. https://towardsdatascience.com/how-to-train-your-own-object-...). [1] Faster R-CNN: https://arxiv.org/abs/1506.01497 [2] Single Shot Multibox: https://arxiv.org/abs/1512.02325 [3] YOLO9000: https://arxiv.org/abs/1612.08242

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks — mentioned by edshiro

Mon, 30 Oct 2017 08:12:11 GMT

Good work! I did this project as well about a week ago. I will shamelessly also share my writings on this project: https://medium.com/towards-data-science/teaching-cars-to-see... . The HOG + SVM method is quite slow and not as accurate as a deep learning approach. Before jumping onto semantic segmentation, I recommend re-implementing this project or more generally solve this problem using a Regional Convolution Neural Network architecture (R-CNN) like Faster R-CNN[1] or YOLO[2] for instance. [1]: https://arxiv.org/abs/1506.01497 [2]: https://arxiv.org/abs/1506.02640

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks — mentioned by maffydub

Mon, 21 Aug 2017 09:29:25 GMT

To save someone googling, the referenced "Is object detection for free" paper is at http://leon.bottou.org/publications/pdf/cvpr-2015.pdf I also just came across "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", which looks interesting and is at https://arxiv.org/pdf/1506.01497.pdf (I'll be reading these after work tonight.)

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