Multi-label image recognition is a practical and challenging task compared to
single-label image classification. However, previous works may be suboptimal
because of a great number of object proposals or complex attentional region
generation modules. In this paper, we propose a simple but efficient two-stream
framework to recognize multi-category objects from global image to local
regions, similar to how human beings perceive objects. To bridge the gap
between global and local streams, we propose a multi-class attentional region
module which aims to make the number of attentional regions as small as
possible and keep the diversity of these regions as high as possible. Our
method can efficiently and effectively recognize multi-class objects with an
affordable computation cost and a parameter-free region localization module.
Over three benchmarks on multi-label image classification, we create new
state-of-the-art results with a single model only using image semantics without
label dependency. In addition, the effectiveness of the proposed method is
extensively demonstrated under different factors such as global pooling
strategy, input size and network architecture. Code has been made available
at~https://github.com/gaobb/MCAR.

本文提出了一种简单而高效的双流框架方法，使用全局图像到局部区域的方法识别多类别目标，并提出了多类关注区域模块（MCAR）来解决关注区域数量过多，生成复杂问题，这种方法仅使用图像语义就可以创建新的最佳结果，并在不同因素下进行了广泛的展示。

学习发现多类别注意力区域用于多标签图像识别

Learning to Discover Multi-Class Attentional Regions for Multi-Label  Image Recognition

Most of current person re-identification (ReID) methods neglect a
spatial-temporal constraint. Given a query image, conventional methods compute
the feature distances between the query image and all the gallery images and
return a similarity ranked table. When the gallery database is very large in
practice, these approaches fail to obtain a good performance due to appearance
ambiguity across different camera views. In this paper, we propose a novel
two-stream spatial-temporal person ReID (st-ReID) framework that mines both
visual semantic information and spatial-temporal information. To this end, a
joint similarity metric with Logistic Smoothing (LS) is introduced to integrate
two kinds of heterogeneous information into a unified framework. To approximate
a complex spatial-temporal probability distribution, we develop a fast
Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint,
the st-ReID model eliminates lots of irrelevant images and thus narrows the
gallery database. Without bells and whistles, our st-ReID method achieves
rank-1 accuracy of 98.1\% on Market-1501 and 94.4\% on DukeMTMC-reID, improving
from the baselines 91.2\% and 83.8\%, respectively, outperforming all previous
state-of-the-art methods by a large margin.

本研究提出了一种新的两流空间 - 时间人物重新识别（st-ReID）框架，该框架挖掘了视觉语义信息和空间 - 时间信息，引入 Logistic Smoothing（LS）联合相似性度量，使用快速直方图 - Parzen（HP）方法近似复杂的空间 - 时间概率分布，消除了很多无关图像，在 Market-1501 和 DukeMTMC-reID 数据集上均取得了最好的准确性，进步幅度达到了先前方法的很大程度。

时空人员再识别

Spatial-Temporal Person Re-identification

In skeleton-based action recognition, graph convolutional networks (GCNs),
which model the human body skeletons as spatiotemporal graphs, have achieved
remarkable performance. However, in existing GCN-based methods, the topology of
the graph is set manually, and it is fixed over all layers and input samples.
This may not be optimal for the hierarchical GCN and diverse samples in action
recognition tasks. In addition, the second-order information (the lengths and
directions of bones) of the skeleton data, which is naturally more informative
and discriminative for action recognition, is rarely investigated in existing
methods. In this work, we propose a novel two-stream adaptive graph
convolutional network (2s-AGCN) for skeleton-based action recognition. The
topology of the graph in our model can be either uniformly or individually
learned by the BP algorithm in an end-to-end manner. This data-driven method
increases the flexibility of the model for graph construction and brings more
generality to adapt to various data samples. Moreover, a two-stream framework
is proposed to model both the first-order and the second-order information
simultaneously, which shows notable improvement for the recognition accuracy.
Extensive experiments on the two large-scale datasets, NTU-RGBD and
Kinetics-Skeleton, demonstrate that the performance of our model exceeds the
state-of-the-art with a significant margin.

本研究提出了一种新的双流自适应图卷积网络 (2s-AGCN)，用于基于骨架的动作识别，通过数据驱动的方法学习网络拓扑结构，同时建立了包含一阶和二阶信息的双流框架，通过对 NTU-RGBD 和 Kinetics-Skeleton 数据集上的实验，证明了本模型的优越性。