Accomplishing household tasks such as 'bringing a cup of water' requires
planning step-by-step actions by maintaining knowledge about the spatial
arrangement of objects and the consequences of previous actions. Perception
models of the current embodied AI agents, however, often make mistakes due to a
lack of such knowledge but rely on imperfect learning of imitating agents or an
algorithmic planner without knowledge about the changed environment by the
previous actions. To address the issue, we propose CPEM (Context-aware Planner
and Environment-aware Memory) to incorporate the contextual information of
previous actions for planning and maintaining spatial arrangement of objects
with their states (e.g., if an object has been moved or not) in an environment
to the perception model for improving both visual navigation and object
interaction. We observe that CPEM achieves state-of-the-art task success
performance in various metrics using a challenging interactive instruction
following benchmark both in seen and unseen environments by large margins (up
to +10.70% in unseen env.). CPEM with the templated actions, named ECLAIR, also
won the 1st generalist language grounding agents challenge at Embodied AI
Workshop in CVPR'23.

通过引入 CPEM（Context-aware Planner and Environment-aware Memory）将先前行动的情境信息与规划和维护对象的空间布局（例如，对象是否已经移动）相结合到感知模型中，改进了视觉导航和对象交互，在可见和不可见环境中实现了如题目要求的各项任务成功性能的最新水平。

上下文感知规划与环境感知记忆对于指令跟随的具身化智能体

Context-Aware Planning and Environment-Aware Memory for Instruction  Following Embodied Agents

Spatial arrangement of cells of various types, such as tumor infiltrating
lymphocytes and the advancing edge of a tumor, are important features for
detecting and characterizing cancers. However, convolutional neural networks
(CNNs) do not explicitly extract intricate features of the spatial arrangements
of the cells from histopathology images. In this work, we propose to classify
cancers using graph convolutional networks (GCNs) by modeling a tissue section
as a multi-attributed spatial graph of its constituent cells. Cells are
detected using their nuclei in H&E stained tissue image, and each cell's
appearance is captured as a multi-attributed high-dimensional vertex feature.
The spatial relations between neighboring cells are captured as edge features
based on their distances in a graph. We demonstrate the utility of this
approach by obtaining classification accuracy that is competitive with CNNs,
specifically, Inception-v3, on two tasks-cancerous versus non-cancerous and in
situ versus invasive-on the BACH breast cancer dataset.

本论文在组织切片图像中，使用图卷积网络 (Graph Convolutional Networks, GCNs) 对细胞的多属性空间图进行建模，通过提取细胞空间排列的复杂特征以实现肿瘤分类，得到了与 Inception-v3 相当的分类准确率。

组织病理学中的直方图图表

Histographs: Graphs in Histopathology

We introduce SDM-NET, a deep generative neural network which produces
structured deformable meshes. Specifically, the network is trained to generate
a spatial arrangement of closed, deformable mesh parts, which respect the
global part structure of a shape collection, e.g., chairs, airplanes, etc. Our
key observation is that while the overall structure of a 3D shape can be
complex, the shape can usually be decomposed into a set of parts, each
homeomorphic to a box, and the finer-scale geometry of the part can be
recovered by deforming the box. The architecture of SDM-NET is that of a
two-level variational autoencoder (VAE). At the part level, a PartVAE learns a
deformable model of part geometries. At the structural level, we train a
Structured Parts VAE (SP-VAE), which jointly learns the part structure of a
shape collection and the part geometries, ensuring a coherence between global
shape structure and surface details. Through extensive experiments and
comparisons with the state-of-the-art deep generative models of shapes, we
demonstrate the superiority of SDM-NET in generating meshes with visual
quality, flexible topology, and meaningful structures, which benefit shape
interpolation and other subsequently modeling tasks.

SDM-NET 是一种深度生成神经网络，用于生成符合形状集合全局部分结构的有结构变形的网格，并通过部分 VAE 在部分几何形状和结构 VAE 的联合学习来确保全局形状结构和表面详情之间的一致性。