Papers with Code 아카이브

Boundary Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 1
평가표: 8
보존된 평가 기록: 20

Boundary Detection is a vital part of extracting information encoded in images, allowing for the computation of quantities of interest including density, velocity, pressure, etc.

Source: A Locally Adapting Technique for Boundary Detection using Image Segmentation

기법

관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.

bilayer decoupling

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers

\begin{equation} QA\left( x \right) = \sigma\left( f\left( x \right)^{1x1} \right) + x \end{equation} Quick Attention takes in the feature map as an input WxHxC (Width x Height x Channels) and creates two instances of the input feature map then it performs the 1x1xC convolution on the first instance and calculates the sigmoid activations after that it is added with the second instance to generate the final attention map as output which is of same dimensions as of input.

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.