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Stanford University › class › cs230 › files_winter_2018 › projects › 6938920.pdfnumber of camera angles). We then use a four-input CNN to output a k-hot vector representation
![web.stanford.eduweb.stanford.edu/class/cs230/files_winter_2018/projects/6940392.pdf · observer and an object to facilitate vision-based obstacle perception [2]. Other remaining approaches](https://img.pdfslide.net/doc/110x75/600da385bb56f910dd31168c/web-observer-and-an-object-to-facilitate-vision-based-obstacle-perception-2.jpg)
![CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6926979.pdf · Our neural network architecture, presented in Fig. 2, is inspired by the VGG neural network [13]. However,](https://img.pdfslide.net/doc/110x75/5f60cb4420a5f158d821b2c9/cs230-deep-our-neural-network-architecture-presented-in-fig-2-is-inspired-by.jpg)
![cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6937153.pdf · 3.3.2 Mask RCNN Mask R-CNN is an extension of the Faster RCNN model [2]. Faster R-CNN is a Region Proposal](https://img.pdfslide.net/doc/110x75/5e448090b525a912e76a06e0/cs230-332-mask-rcnn-mask-r-cnn-is-an-extension-of-the-faster-rcnn-model-2.jpg)
