While propagation-based approaches have achieved state-of-the-art performance
for video object segmentation, the literature lacks a fair comparison of
different methods using the same settings. In this paper, we carry out an
empirical study for propagation-based methods. We view these approaches from a
unified perspective and conduct detailed ablation study for core methods, input
cues, multi-object combination and training strategies. With careful designs,
our improved end-to-end memory networks achieve a global mean of 76.1 on DAVIS
2017 val set.

本文通过对传输提取方法的实证研究，设计了端到端的记忆网络，对核心方法、输入线索、多物体组合和训练策略等方面进行了详细的剖析研究，在 DAVIS 2017 数据集上取得了 76.1 的显著性能提升。

基于传播方法的视频目标分割的实证研究

An Empirical Study of Propagation-based Methods for Video Object  Segmentation

In this paper we present a new dataset and user simulator e-QRAQ (explainable
Query, Reason, and Answer Question) which tests an Agent's ability to read an
ambiguous text; ask questions until it can answer a challenge question; and
explain the reasoning behind its questions and answer. The User simulator
provides the Agent with a short, ambiguous story and a challenge question about
the story. The story is ambiguous because some of the entities have been
replaced by variables. At each turn the Agent may ask for the value of a
variable or try to answer the challenge question. In response the User
simulator provides a natural language explanation of why the Agent's query or
answer was useful in narrowing down the set of possible answers, or not. To
demonstrate one potential application of the e-QRAQ dataset, we train a new
neural architecture based on End-to-End Memory Networks to successfully
generate both predictions and partial explanations of its current understanding
of the problem. We observe a strong correlation between the quality of the
prediction and explanation.

本文中，我们提出了一个新的数据集和用户模拟器 e-QRAQ（可解释的查询、推理和回答问题），该数据集测试了代理的读取模糊文本的能力，通过提问来回答挑战性问题并解释其问题和答案的推理。我们使用端到端记忆网络来训练一个神经结构，以成功地生成对问题的预测和部分解释，并发现预测和解释的质量之间存在着很强的相关性。

e-QRAQ: 一个带解释的多轮推理数据集与模拟器

e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations

Recently, end-to-end memory networks have shown promising results on Question
Answering task, which encode the past facts into an explicit memory and perform
reasoning ability by making multiple computational steps on the memory.
However, memory networks conduct the reasoning on sentence-level memory to
output coarse semantic vectors and do not further take any attention mechanism
to focus on words, which may lead to the model lose some detail information,
especially when the answers are rare or unknown words. In this paper, we
propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the
past facts into sentence-level memory and word-level memory respectively. Then,
(k)-max pooling is exploited following reasoning module on the sentence-level
memory to sample the (k) most relevant sentences to a question and feed these
sentences into attention mechanism on the word-level memory to focus the words
in the selected sentences. Finally, the prediction is jointly learned over the
outputs of the sentence-level reasoning module and the word-level attention
mechanism. The experimental results demonstrate that our approach successfully
conducts answer selection on unknown words and achieves a better performance
than memory networks.

论文提出了一种新的分层记忆网络 (Hierarchical Memory Networks, HMN)，它利用注意力机制在句子级别和单词级别的记忆上分别进行推理，以处理在答案中出现较少或不知道的词汇，并在实验中证明其表现优于记忆网络。