Recently, implicit graph neural networks (GNNs) have been proposed to capture
long-range dependencies in underlying graphs. In this paper, we introduce and
justify two weaknesses of implicit GNNs: the constrained expressiveness due to
their limited effective range for capturing long-range dependencies, and their
lack of ability to capture multiscale information on graphs at multiple
resolutions. To show the limited effective range of previous implicit GNNs, We
first provide a theoretical analysis and point out the intrinsic relationship
between the effective range and the convergence of iterative equations used in
these models. To mitigate the mentioned weaknesses, we propose a multiscale
graph neural network with implicit layers (MGNNI) which is able to model
multiscale structures on graphs and has an expanded effective range for
capturing long-range dependencies. We conduct comprehensive experiments for
both node classification and graph classification to show that MGNNI
outperforms representative baselines and has a better ability for multiscale
modeling and capturing of long-range dependencies.

本文介绍和证明了隐式图神经网络存在的两个弱点：他们的限制表达能力由于他们对捕获长程依赖性的有效范围有限以及他们无法在多个分辨率上对图进行多尺度信息建模。为了缓解这些弱点，我们提出了一种带有隐式层次的多尺度图神经网络 (MGNNI)，它能够模拟图的多尺度结构，具有更广泛的有效范围，能够捕获长程依赖性，并且在节点分类和图分类方面，我们进行了全面的实验来表明 MGNNI 优于代表性基准，并具有更好的多尺度建模和捕获长程依赖性的能力。

MGNNI: 带隐式层的多尺度图神经网络

MGNNI: Multiscale Graph Neural Networks with Implicit Layers

Video question answering (VideoQA) is challenging given its multimodal
combination of visual understanding and natural language processing. While most
existing approaches ignore the visual appearance-motion information at
different temporal scales, it is unknown how to incorporate the multilevel
processing capacity of a deep learning model with such multiscale information.
Targeting these issues, this paper proposes a novel Multilevel Hierarchical
Network (MHN) with multiscale sampling for VideoQA. MHN comprises two modules,
namely Recurrent Multimodal Interaction (RMI) and Parallel Visual Reasoning
(PVR). With a multiscale sampling, RMI iterates the interaction of
appearance-motion information at each scale and the question embeddings to
build the multilevel question-guided visual representations. Thereon, with a
shared transformer encoder, PVR infers the visual cues at each level in
parallel to fit with answering different question types that may rely on the
visual information at relevant levels. Through extensive experiments on three
VideoQA datasets, we demonstrate improved performances than previous
state-of-the-arts and justify the effectiveness of each part of our method.

本研究提出了一种新的多级分层网络（MHN），结合多重时间尺度和深度学习模型的多层次处理能力，通过 Multiscale Sampling 实现逐层交互式的表示学习，从而实现视频问题回答中的视觉推理。