SNIP, or Scale Normalization for Image Pyramids, is a technique used for object detection in computer vision. It is a multi-scale training scheme that selectively back-propagates the gradients of object instances of different sizes as a function of the image scale.
What is multi-scale training?
Multi-scale training (MST) is a technique used for object detection in computer vision that involves observing each image at different resolutions. This is because at a high resolution, large objects are hard to classify, while at a low resolution, small objects are hard to classify. Fortunately, each object instance appears at several different scales, and some of those appearances fall in the desired scale range.
How does SNIP work?
In order to improve upon MST, selectively back-propagates the gradients of object instances of different sizes. SNIP is a modified version of MST where only the object instances that have a resolution close to the pre-training dataset, which is typically 224x224, are used for training the detector.
Effectively, SNIP uses all the object instances during training, which helps capture all the variations in appearance and pose, while reducing the domain-shift in the scale-space for the pre-trained network. This allows for more accurate object detection across different scales.
What are the benefits of using SNIP?
The benefits of using SNIP for object detection include:
- Improved accuracy across different scales
- Reduced domain-shift in the scale-space for the pre-trained network
- Reduction in extreme scale objects, making it easier to detect objects at the desired scale range
SNIP, or Scale Normalization for Image Pyramids, is a multi-scale training scheme used for object detection in computer vision. It improves upon the traditional MST technique by selectively back-propagating the gradients of object instances of different sizes. This results in improved accuracy across different scales, reduced domain-shift in the scale-space for the pre-trained network, and a reduction in extreme scale objects. Overall, SNIP is a powerful technique for improving the accuracy of object detection in computer vision applications.
