Thanks to the rapid development of diffusion models, unprecedented progress
has been witnessed in image synthesis. Prior works mostly rely on pre-trained
linguistic models, but a text is often too abstract to properly specify all the
spatial properties of an image, e.g., the layout configuration of a scene,
leading to the sub-optimal results of complex scene generation. In this paper,
we achieve accurate complex scene generation by proposing a semantically
controllable Layout-AWare diffusion model, termed LAW-Diffusion. Distinct from
the previous Layout-to-Image generation (L2I) methods that only explore
category-aware relationships, LAW-Diffusion introduces a spatial dependency
parser to encode the location-aware semantic coherence across objects as a
layout embedding and produces a scene with perceptually harmonious object
styles and contextual relations. To be specific, we delicately instantiate each
object's regional semantics as an object region map and leverage a
location-aware cross-object attention module to capture the spatial
dependencies among those disentangled representations. We further propose an
adaptive guidance schedule for our layout guidance to mitigate the trade-off
between the regional semantic alignment and the texture fidelity of generated
objects. Moreover, LAW-Diffusion allows for instance reconfiguration while
maintaining the other regions in a synthesized image by introducing a
layout-aware latent grafting mechanism to recompose its local regional
semantics. To better verify the plausibility of generated scenes, we propose a
new evaluation metric for the L2I task, dubbed Scene Relation Score (SRS) to
measure how the images preserve the rational and harmonious relations among
contextual objects. Comprehensive experiments demonstrate that our
LAW-Diffusion yields the state-of-the-art generative performance, especially
with coherent object relations.

通过提出一种语义可控的布局感知 (Diffusion) 模型 (LAW-Diffusion)，本研究实现了准确的复杂场景生成，通过引入空间依赖解析器来编码在对象之间的位置感知的语义一致性作为布局嵌入，并产生具有感知上和谐对象风格和语境关系的场景。

LAW-Diffusion：基于布局扩散的复杂场景生成

LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts

Anomaly detection research works generally propose algorithms or end-to-end
systems that are designed to automatically discover outliers in a dataset or a
stream. While literature abounds concerning algorithms or the definition of
metrics for better evaluation, the quality of the ground truth against which
they are evaluated is seldom questioned. In this paper, we present a systematic
analysis of available public (and additionally our private) ground truth for
anomaly detection in the context of network environments, where data is
intrinsically temporal, multivariate and, in particular, exhibits spatial
properties, which, to the best of our knowledge, we are the first to explore.
Our analysis reveals that, while anomalies are, by definition, temporally rare
events, their spatial characterization clearly shows some type of anomalies are
significantly more popular than others. We find that simple clustering can
reduce the need for human labeling by a factor of 2x-10x, that we are first to
quantitatively analyze in the wild.

本文针对网络环境下的异常检测问题，通过系统分析已有的公共和私有基准数据，探索了其时态性、多变量性和空间属性特征，并提出了基于聚类的简化标注方法，实验结果为 2-10 倍。