DeepMask

2000년 도입 · 논문 6편에서 사용

DeepMask is an object proposal algorithm based on a convolutional neural network. Given an input image patch, DeepMask generates a class-agnostic mask and an associated score which estimates the likelihood of the patch fully containing a centered object (without any notion of an object category). The core of the model is a ConvNet which jointly predicts the mask and the object score. A large part of the network is shared between those two tasks: only the last few network layers are specialized for separately outputting a mask and score prediction.

출처: Learning to Segment Object Candidates via Recursive Neural Networks

소개 논문: Learning to Segment Object Candidates via Recursive Neural Networks