Human-Object Interaction Detection tackles the problem of joint localization
and classification of human object interactions. Existing HOI transformers
either adopt a single decoder for triplet prediction, or utilize two parallel
decoders to detect individual objects and interactions separately, and compose
triplets by a matching process. In contrast, we decouple the triplet prediction
into human-object pair detection and interaction classification. Our main
motivation is that detecting the human-object instances and classifying
interactions accurately needs to learn representations that focus on different
regions. To this end, we present Disentangled Transformer, where both encoder
and decoder are disentangled to facilitate learning of two sub-tasks. To
associate the predictions of disentangled decoders, we first generate a unified
representation for HOI triplets with a base decoder, and then utilize it as
input feature of each disentangled decoder. Extensive experiments show that our
method outperforms prior work on two public HOI benchmarks by a sizeable
margin. Code will be available.

该研究旨在解决人 - 物交互检测中的联合定位和分类问题，通过提出解耦三元组预测为人 - 物对检测和交互分类两个子任务来学习关注不同区域的表示，从而设计出 Disentangled Transformer，与现有 HOI transformers 相比，其方法在两个公共 HOI 基准测试中表现更佳。

基于解缠的 Transformer 的人 - 物互动检测

Human-Object Interaction Detection via Disentangled Transformer

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a
list of non-discrete attributes for each entity. Intuitively, these attributes
such as height, price or population count are able to richly characterize
entities in knowledge graphs. This additional source of information may help to
alleviate the inherent sparsity and incompleteness problem that are prevalent
in knowledge graphs. Unfortunately, many state-of-the-art relational learning
models ignore this information due to the challenging nature of dealing with
non-discrete data types in the inherently binary-natured knowledge graphs. In
this paper, we propose a novel multi-task neural network approach for both
encoding and prediction of non-discrete attribute information in a relational
setting. Specifically, we train a neural network for triplet prediction along
with a separate network for attribute value regression. Via multi-task
learning, we are able to learn representations of entities, relations and
attributes that encode information about both tasks. Moreover, such attributes
are not only central to many predictive tasks as an information source but also
as a prediction target. Therefore, models that are able to encode, incorporate
and predict such information in a relational learning context are highly
attractive as well. We show that our approach outperforms many state-of-the-art
methods for the tasks of relational triplet classification and attribute value
prediction.

本文中，我们提出了一种新颖的基于多任务神经网络的方法，用于在关系学习环境中编码、融合和预测非离散属性信息，该方法在关系三元组分类和属性值预测任务中优于现有的许多方法。