We develop a Synthetic Fusion Pyramid Network (SPF-Net) with a scale-aware
loss function design for accurate crowd counting. Existing crowd-counting
methods assume that the training annotation points were accurate and thus
ignore the fact that noisy annotations can lead to large model-learning bias
and counting error, especially for counting highly dense crowds that appear far
away. To the best of our knowledge, this work is the first to properly handle
such noise at multiple scales in end-to-end loss design and thus push the crowd
counting state-of-the-art. We model the noise of crowd annotation points as a
Gaussian and derive the crowd probability density map from the input image. We
then approximate the joint distribution of crowd density maps with the full
covariance of multiple scales and derive a low-rank approximation for
tractability and efficient implementation. The derived scale-aware loss
function is used to train the SPF-Net. We show that it outperforms various loss
functions on four public datasets: UCF-QNRF, UCF CC 50, NWPU and ShanghaiTech
A-B datasets. The proposed SPF-Net can accurately predict the locations of
people in the crowd, despite training on noisy training annotations.

本文开发一种 Synthetic Fusion Pyramid Network（SPF-Net）和设计具有尺度感知损失函数，以实现准确的人群计数。我们以 Gaussian 模型噪声标注点，从输入图像中导出人群概率密度图。然后，我们使用多尺度完全协方差近似联合分布，以实现可构建性和高效实现的低秩近似。所提出的 SPF-Net 可以准确地预测人群中人员的位置，尽管是在嘈杂的训练注释下训练的。

基于联合似然密度图和合成融合金字塔网络的尺度感知人群计数

Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network

Inspired by how humans summarize long documents, we propose an accurate and
fast summarization model that first selects salient sentences and then rewrites
them abstractively (i.e., compresses and paraphrases) to generate a concise
overall summary. We use a novel sentence-level policy gradient method to bridge
the non-differentiable computation between these two neural networks in a
hierarchical way, while maintaining language fluency. Empirically, we achieve
the new state-of-the-art on all metrics (including human evaluation) on the
CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores.
Moreover, by first operating at the sentence-level and then the word-level, we
enable parallel decoding of our neural generative model that results in
substantially faster (10-20x) inference speed as well as 4x faster training
convergence than previous long-paragraph encoder-decoder models. We also
demonstrate the generalization of our model on the test-only DUC-2002 dataset,
where we achieve higher scores than a state-of-the-art model.

本论文提出一种快速、精准的摘要生成模型，该模型首先选择重要句子，然后使用新颖的基于句子级的策略梯度方法实现两个神经网络之间的其它计算，最后生成简洁的总体摘要。该模型在 CNN/Daily Mail 数据集上的表现达到了最新的最佳水平，并且具有更高的抽象性得分，同时也展示出相较于之前的长段落编码 - 解码模型，更快的推断速度和训练收敛速度优势，而且在 DUC-2002 数据集上表现更优。