Heterogeneous graph neural networks (HGNNs) have been widely applied in
heterogeneous information network tasks, while most HGNNs suffer from poor
scalability or weak representation when they are applied to large-scale
heterogeneous graphs. To address these problems, we propose a novel
Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning
(RHCO) for large-scale heterogeneous graph representation learning. Unlike
traditional heterogeneous graph neural networks, we adopt the contrastive
learning mechanism to deal with the complex heterogeneity of large-scale
heterogeneous graphs. We first learn relation-aware node embeddings under the
network schema view. Then we propose a novel positive sample selection strategy
to choose meaningful positive samples. After learning node embeddings under the
positive sample graph view, we perform a cross-view contrastive learning to
obtain the final node representations. Moreover, we adopt the label smoothing
technique to boost the performance of RHCO. Extensive experiments on three
large-scale academic heterogeneous graph datasets show that RHCO achieves best
performance over the state-of-the-art models.

本文提出了一种新的基于对比学习的关系感知异构图神经网络 RHCO，用于大规模异构图表示学习，相对于传统的异构图神经网络，可以更好地处理复杂异构网络结构，并采用标签平滑技术提高了性能，实验证明在三个大规模学术异构图数据集上 RHCO 取得了最好的性能表现。

RHCO: 一种关系感知的异构图神经网络，配合对比学习用于大规模图

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

In this paper, we introduce a new problem, named audio-visual video parsing,
which aims to parse a video into temporal event segments and label them as
either audible, visible, or both. Such a problem is essential for a complete
understanding of the scene depicted inside a video. To facilitate exploration,
we collect a Look, Listen, and Parse (LLP) dataset to investigate audio-visual
video parsing in a weakly-supervised manner. This task can be naturally
formulated as a Multimodal Multiple Instance Learning (MMIL) problem.
Concretely, we propose a novel hybrid attention network to explore unimodal and
cross-modal temporal contexts simultaneously. We develop an attentive MMIL
pooling method to adaptively explore useful audio and visual content from
different temporal extent and modalities. Furthermore, we discover and mitigate
modality bias and noisy label issues with an individual-guided learning
mechanism and label smoothing technique, respectively. Experimental results
show that the challenging audio-visual video parsing can be achieved even with
only video-level weak labels. Our proposed framework can effectively leverage
unimodal and cross-modal temporal contexts and alleviate modality bias and
noisy labels problems.

本文介绍了音频 - 视觉视频解析的问题，并提出了一个新的混合注意力网络方法和一种自适应 MMIL 池化方法来解决多模态多实例学习问题，以及利用标签平滑技术来减轻模态偏置和嘈杂标签问题。实验结果表明，即使只有视频级弱标签，也可以实现具有挑战性的音频 - 视觉视频解析。