Recently, Zero-Shot Node Classification (ZNC) has been an emerging and
crucial task in graph data analysis. This task aims to predict nodes from
unseen classes which are unobserved in the training process. Existing work
mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes
and labels' semantics thus enabling knowledge transfer from seen to unseen
classes. However, the multi-faceted semantic orientation in the
feature-semantic alignment has been neglected by previous work, i.e. the
content of a node usually covers diverse topics that are relevant to the
semantics of multiple labels. It's necessary to separate and judge the semantic
factors that tremendously affect the cognitive ability to improve the
generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted
framework (KMF) that enhances the richness of label semantics via the extracted
KG (Knowledge Graph)-based topics. And then the content of each node is
reconstructed to a topic-level representation that offers multi-faceted and
fine-grained semantic relevancy to different labels. Due to the particularity
of the graph's instance (i.e., node) representation, a novel geometric
constraint is developed to alleviate the problem of prototype drift caused by
node information aggregation. Finally, we conduct extensive experiments on
several public graph datasets and design an application of zero-shot
cross-domain recommendation. The quantitative results demonstrate both the
effectiveness and generalization of KMF with the comparison of state-of-the-art
baselines.

提出了一种基于知识图谱的多维度框架（KMF），通过提取话题来增强标签语义的丰富性，并将每个节点的内容重构为话题级别的表示，提供多维和细粒度的语义相关性，通过新的几何约束来缓解节点信息聚合引起的原型漂移问题，多个公共图数据集上的实验证明了 KMF 的有效性和泛化性。

KMF: 知识感知的多方面表示学习用于零样本节点分类

KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot  Node Classification

We present ShapeClipper, a novel method that reconstructs 3D object shapes
from real-world single-view RGB images. Instead of relying on laborious 3D,
multi-view or camera pose annotation, ShapeClipper learns shape reconstruction
from a set of single-view segmented images. The key idea is to facilitate shape
learning via CLIP-based shape consistency, where we encourage objects with
similar CLIP encodings to share similar shapes. We also leverage off-the-shelf
normals as an additional geometric constraint so the model can learn better
bottom-up reasoning of detailed surface geometry. These two novel consistency
constraints, when used to regularize our model, improve its ability to learn
both global shape structure and local geometric details. We evaluate our method
over three challenging real-world datasets, Pix3D, Pascal3D+, and OpenImages,
where we achieve superior performance over state-of-the-art methods.

ShapeClipper 是一种利用单视角图像实现三维物体形状重建的新方法，其中采用基于 CLIP 的形状一致性来促进物体的形状学习，并利用现成的法线作为额外的几何约束，以实现对详细表面几何结构的自下而上推理。我们在 Pix3D、Pascal3D + 和 OpenImages 等三个数据集上进行了评估，并取得了比最先进方法更好的性能。

ShapeClipper：基于几何和 CLIP 一致性的单视图图像可扩展的三维形状学习

ShapeClipper: Scalable 3D Shape Learning from Single-View Images via  Geometric and CLIP-based Consistency

We present a self-supervised learning framework to estimate the individual
object motion and monocular depth from video. We model the object motion as a 6
degree-of-freedom rigid-body transformation. The instance segmentation mask is
leveraged to introduce the information of object. Compared with methods which
predict dense optical flow map to model the motion, our approach significantly
reduces the number of values to be estimated. Our system eliminates the scale
ambiguity of motion prediction through imposing a novel geometric constraint
loss term. Experiments on KITTI driving dataset demonstrate our system is
capable to capture the object motion without external annotation. Our system
outperforms previous self-supervised approaches in terms of 3D scene flow
prediction, and contribute to the disparity prediction in dynamic area.

本文提出了一种自监督学习框架，从视频中估算单个对象的运动和单眼深度，并将对象运动建模为六个自由度刚体变换；此外，该方法还使用实例分割掩码引入对象信息，并通过引入新的几何约束损失项消除运动预测的尺度歧义，实验结果表明，该框架在不需要外部注释的情况下处理数据并能够捕捉对象的运动，与自监督研究方法相比，在 3D 场景流预测方面有更好的表现，对动态区域的视差预测也有所贡献。