Aggregation of multi-stage features has been revealed to play a significant
role in semantic segmentation. Unlike previous methods employing point-wise
summation or concatenation for feature aggregation, this study proposes the
Category Feature Transformer (CFT) that explores the flow of category embedding
and transformation among multi-stage features through the prevalent multi-head
attention mechanism. CFT learns unified feature embeddings for individual
semantic categories from high-level features during each aggregation process
and dynamically broadcasts them to high-resolution features. Integrating the
proposed CFT into a typical feature pyramid structure exhibits superior
performance over a broad range of backbone networks. We conduct extensive
experiments on popular semantic segmentation benchmarks. Specifically, the
proposed CFT obtains a compelling 55.1% mIoU with greatly reduced model
parameters and computations on the challenging ADE20K dataset.

利用类别特征转换器（CFT）来探索多阶段特征之间的类别嵌入和转换，通过常见的多头注意力机制学习统一的特征嵌入，并将其动态广播到高分辨率特征，将 CFT 集成到典型的特征金字塔结构中可以在多种骨干网络上展现卓越性能，经过在流行的语义分割基准数据集上的大规模实验，CFT 在具有挑战性的 ADE20K 数据集上获得了引人注目的 55.1％mIoU，同时大大减少了模型参数和计算量。

语义分割的类别特征转换器

Category Feature Transformer for Semantic Segmentation

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。

使用无监督多阶段特征学习进行行人检测

Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

We classify digits of real-world house numbers using convolutional neural
networks (ConvNets). ConvNets are hierarchical feature learning neural networks
whose structure is biologically inspired. Unlike many popular vision approaches
that are hand-designed, ConvNets can automatically learn a unique set of
features optimized for a given task. We augmented the traditional ConvNet
architecture by learning multi-stage features and by using Lp pooling and
establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2%
error improvement). Furthermore, we analyze the benefits of different pooling
methods and multi-stage features in ConvNets. The source code and a tutorial
are available at eblearn.sf.net.

本文使用卷积神经网络 (ConvNets) 分类实际房屋数字，通过学习多层特征和使用 Lp pooling 等方法优化传统神经网络结构并在 SVHN 数据集上实现了 94.85% 的准确率，比以前的方法提高了 45.2% 的准确性，同时分析了 ConvNets 中不同池化方法和多层特征的优劣。