Deformable Position-Sensitive RoI Pooling

Introduced by Dai et al. in Deformable Convolutional Networks

Deformable Position-Sensitive RoI Pooling is similar to PS RoI Pooling but it adds an offset to each bin position in the regular bin partition. Offset learning follows the “fully convolutional” spirit. In the top branch, a convolutional layer generates the full spatial resolution offset fields. For each RoI (also for each class), PS RoI pooling is applied on such fields to obtain normalized offsets, which are then transformed to the real offsets, in the same way as in deformable RoI pooling.

Source: Deformable Convolutional Networks

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Object Detection	1	33.33%
Semantic Segmentation	1	33.33%
Vessel Detection	1	33.33%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
🤖 No Components Found	You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories

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RoI Feature Extractors