CANet(CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning)[1] consists of a two-branch dense comparison module which performs multi-level feature comparison, and an iterative optimization module which iteratively refines the predicted results. There are some details of reading and implementing it.

SG-One(SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation)[1] adopt a masked average pooling strategy for producing the guidance features, then leverage the cosine similarity to build the relationship. There are some details of reading and implementing it.

co-FCN(Conditional Networks for Few-Shot Semantic Segmentation)[1] handle sparse pixel-wise annotations to achieve nearly the same accuracy. There are some details of reading and implementing it.

OSLSM(One-Shot Learning for Semantic Segmentation)[1] firstly proposed two-branch approach to one-shot semantic segmentation. Conditioning branch trains a network to get parameter $\theta$, and Segmentaion branch outputs the final mask based on parameter $\theta$. There are some details of reading and implementing it.

题目I

题目描述

从上到下打印出二叉树的每个节点，同一层的节点按照从左到右的顺序打印。

题目

题目描述

输入两个整数序列，第一个序列表示栈的压入顺序，请判断第二个序列是否为该栈的弹出顺序。假设压入栈的所有数字均不相等。例如，序列 {1,2,3,4,5} 是某栈的压栈序列，序列 {4,5,3,2,1} 是该压栈序列对应的一个弹出序列，但 {4,3,5,1,2} 就不可能是该压栈序列的弹出序列。