Top 7 feature pyramid network in 2022

Author: arxiv.org

Date Submitted: 03/22/2019 01:32 AM

Average star voting: 4 ⭐ ( 60081 reviews)

Summary: Detecting objects in different scales is challenging in particular for small objects. We can use a pyramid of the same image at different scale to detect objects (the left diagram below). However…

Match with the search results: Abstract: Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning ……. read more

Author: jonathan-hui.medium.com

Date Submitted: 04/02/2020 03:40 AM

Average star voting: 5 ⭐ ( 56555 reviews)

Summary: A Feature Pyramid Network, or FPN, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion. This process is independent of the backbone convolutional architectures. It therefore acts as a generic solution for building feature pyramids inside deep convolutional networks to be used in tasks like object detection.

The construction of the pyramid involves a bottom-up pathway and a top-down pathway.

The bottom-up pathway is the feedforward computation of the backbone ConvNet, which computes a feature hierarchy consisting of feature maps at several scales with a scaling step of 2. For the feature
pyramid, one pyramid level is defined for each stage. The output of the last layer of each stage is used as a reference set of feature maps. For ResNets we use the feature activations output by each stage’s last residual block.

The top-down pathway hallucinates higher resolution features by upsampling spatially coarser, but semantically stronger, feature maps from higher pyramid levels. These features are then enhanced with features from the bottom-up pathway via lateral connections. Each lateral connection merges feature maps of the same spatial size from the bottom-up pathway and the top-down pathway. The bottom-up feature map is of lower-level semantics, but its activations are more accurately localized as it was subsampled fewer times.

Match with the search results: Feature Pyramid Network (FPN) is a feature extractor designed for such pyramid concept with accuracy and speed in mind. It replaces the feature extractor of ……. read more

Author: paperswithcode.com

Date Submitted: 03/20/2020 09:26 AM

Average star voting: 4 ⭐ ( 85564 reviews)

Summary: Feature Pyramid Network is designed to combine the features from different levels of a convolutional network, in order to better detect objects at different scales.

Match with the search results: … detector based on multi-level feature pyramid network…. read more

Author: towardsdatascience.com

Date Submitted: 04/23/2019 08:58 AM

Average star voting: 4 ⭐ ( 66238 reviews)

Summary:

Match with the search results: A Feature Pyramid Network, or FPN, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized ……. read more

Author: openaccess.thecvf.com

Date Submitted: 01/19/2019 09:18 PM

Average star voting: 4 ⭐ ( 31750 reviews)

Summary: Feature pyramids have become ubiquitous in multi-scale computer vision tasks such as object detection. Based on their importance, we divide a computer vision network into three parts: a backbone (generating a feature pyramid), a core (refining the feature pyramid) and a head (generating the final output). Most existing networks operating on feature pyramids, named cores, are shallow and mostly focus on communication-based processing in the form of top-down and bottom-up operations. We present a new core architecture called Trident Pyramid Network (TPN), that allows for a deeper design and for a better balance between communication-based processing and self-processing. We show consistent improvements when using our TPN core on the COCO object detection benchmark, outperforming the popular BiFPN baseline by 1.5 AP. Additionally, we empirically show that it is more beneficial to put additional computation into the TPN core, rather than into the backbone, by outperforming a ResNet-101+FPN baseline with our ResNet-50+TPN network by 1.7 AP, while operating under similar computation budgets. This emphasizes the importance of performing computation at the feature pyramid level in modern-day object detection systems. Code will be released.

Match with the search results: This feature pyramid that has rich semantics at all levels and is built quickly from a single input image scale, thereby without sacrificing ……. read more

Author: fastpath.one

Date Submitted: 07/15/2021 11:45 AM

Average star voting: 5 ⭐ ( 16122 reviews)

Summary: Variational Feature Pyramid NetworksPanagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros NikouRecent architectures for object detection adopt …

Match with the search results: In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to con- struct feature pyramids with marginal extra cost….. read more

Author: vision.cornell.edu

Date Submitted: 12/31/2020 02:31 AM

Average star voting: 5 ⭐ ( 69486 reviews)

Summary:

Match with the search results: Feature Pyramid Network is designed to combine the features from different levels of a convolutional network, in order to better detect objects ……. read more