Learning to re-identify or retrieve a group of people across non-overlapped
camera systems has important applications in video surveillance. However, most
existing methods focus on (single) person re-identification (re-id), ignoring
the fact that people often walk in groups in real scenarios. In this work, we
take a step further and consider employing context information for identifying
groups of people, i.e., group re-id. We propose a novel unified framework based
on graph neural networks to simultaneously address the group-based re-id tasks,
i.e., group re-id and group-aware person re-id. Specifically, we construct a
context graph with group members as its nodes to exploit dependencies among
different people. A multi-level attention mechanism is developed to formulate
both intra-group and inter-group context, with an additional self-attention
module for robust graph-level representations by attentively aggregating
node-level features. The proposed model can be directly generalized to tackle
group-aware person re-id using node-level representations. Meanwhile, to
facilitate the deployment of deep learning models on these tasks, we build a
new group re-id dataset that contains more than 3.8K images with 1.5K annotated
groups, an order of magnitude larger than existing group re-id datasets.
Extensive experiments on the novel dataset as well as three existing datasets
clearly demonstrate the effectiveness of the proposed framework for both
group-based re-id tasks. The code is available at
this https URL

本文提出了一种基于图神经网络的统一框架，用于同时解决基于组的重新识别任务，即组重新识别和组感知人重新识别，并构建了一个包含超过 3.8K 张图片和 1.5K 注释组的新组重新识别数据集，清楚地证明了所提出的框架对于基于组的重新识别任务的有效性。

基于多注意力上下文图的组群再识别学习

Learning Multi-Attention Context Graph for Group-Based Re-Identification

In this work, we introduce a novel algorithm for solving the textbook
question answering (TQA) task which describes more realistic QA problems
compared to other recent tasks. We mainly focus on two related issues with
analysis of the TQA dataset. First, solving the TQA problems requires to
comprehend multi-modal contexts in complicated input data. To tackle this issue
of extracting knowledge features from long text lessons and merging them with
visual features, we establish a context graph from texts and images, and
propose a new module f-GCN based on graph convolutional networks (GCN). Second,
scientific terms are not spread over the chapters and subjects are split in the
TQA dataset. To overcome this so called "out-of-domain" issue, before learning
QA problems, we introduce a novel self-supervised open-set learning process
without any annotations. The experimental results show that our model
significantly outperforms prior state-of-the-art methods. Moreover, ablation
studies validate that both methods of incorporating f-GCN for extracting
knowledge from multi-modal contexts and our newly proposed self-supervised
learning process are effective for TQA problems.

该研究提出了一种新算法，用于解决复杂的教材题目答案（TQA）任务，该算法主要围绕多模态上下文中的知识特征提取以及自我监督开放式学习过程展开，并且实验证明其优于目前先进的方法。