Difference between revisions of "Local Features"
(Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M...") |
|||
| Line 9: | Line 9: | ||
* [[Deep Features]] | * [[Deep Features]] | ||
| + | * [[Attention]] | ||
| + | |||
| + | |||
<youtube>ek9jwRA2Jio</youtube> | <youtube>ek9jwRA2Jio</youtube> | ||
| + | |||
| + | == LF-Net == | ||
| + | LF-Net has two main components. The first one is a dense, multi-scale, fully convolutional network that returns keypoint locations, scales, and orientations. It is designed to achieve fast inference time, and to be agnostic to image size. The second is a network that outputs local descriptors given patches cropped around the keypoints produced by the first network. We call them detector and descriptor. ...presented a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision ... propose a sparse-matching method with a novel deep architecture, which we name LF-Net, for Local Feature Network, that is trainable end-to-end and does not require using a hand-crafted detector to generate training data. ...local features have played a crucial role in computer vision, becoming the de facto standard for wide-baseline image matching http://papers.nips.cc/paper/7861-lf-net-learning-local-features-from-images.pdf | Y. Ono, E. Trulls, P Fua, and K.Yi] | ||
Revision as of 19:40, 30 June 2019
YouTube search... ...Google search
LF-Net
LF-Net has two main components. The first one is a dense, multi-scale, fully convolutional network that returns keypoint locations, scales, and orientations. It is designed to achieve fast inference time, and to be agnostic to image size. The second is a network that outputs local descriptors given patches cropped around the keypoints produced by the first network. We call them detector and descriptor. ...presented a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision ... propose a sparse-matching method with a novel deep architecture, which we name LF-Net, for Local Feature Network, that is trainable end-to-end and does not require using a hand-crafted detector to generate training data. ...local features have played a crucial role in computer vision, becoming the de facto standard for wide-baseline image matching http://papers.nips.cc/paper/7861-lf-net-learning-local-features-from-images.pdf | Y. Ono, E. Trulls, P Fua, and K.Yi]
