In this paper we approach the novel problem of segmenting an image based on a
natural language expression. This is different from traditional semantic
segmentation over a predefined set of semantic classes, as e.g., the phrase
"two men sitting on the right bench" requires segmenting only the two people on
the right bench and no one standing or sitting on another bench. Previous
approaches suitable for this task were limited to a fixed set of categories
and/or rectangular regions. To produce pixelwise segmentation for the language
expression, we propose an end-to-end trainable recurrent and convolutional
network model that jointly learns to process visual and linguistic information.
In our model, a recurrent LSTM network is used to encode the referential
expression into a vector representation, and a fully convolutional network is
used to a extract a spatial feature map from the image and output a spatial
response map for the target object. We demonstrate on a benchmark dataset that
our model can produce quality segmentation output from the natural language
expression, and outperforms baseline methods by a large margin.

本文提出了一个基于自然语言表达式的图像分割新问题，在此任务中，我们使用全新的终端到终端训练的递归卷积网络模型来提取像素级的分割结果，它能够同时学习处理视觉和语义信息，实验结果表明我们的方法在对自然语言表达式进行分割时能够产生更高质量的的分割效果。

自然语言表达式的分割

Segmentation from Natural Language Expressions

Pedestrian detection is a problem of considerable practical interest. Adding
to the list of successful applications of deep learning methods to vision, we
report state-of-the-art and competitive results on all major pedestrian
datasets with a convolutional network model. The model uses a few new twists,
such as multi-stage features, connections that skip layers to integrate global
shape information with local distinctive motif information, and an unsupervised
method based on convolutional sparse coding to pre-train the filters at each
stage.

本文利用卷积网络模型，采用多阶特征提取，全局形状信息和本地独特的纹理信息相结合以及基于卷积稀疏编码的无监督方法来预训练过滤器，取得了所有主要行人数据集上的最先进和有竞争力的结果，从而解决了行人检测的实际问题。