Learning 3D human-object interaction relation is pivotal to embodied AI and
interaction modeling. Most existing methods approach the goal by learning to
predict isolated interaction elements, e.g., human contact, object affordance,
and human-object spatial relation, primarily from the perspective of either the
human or the object. Which underexploit certain correlations between the
interaction counterparts (human and object), and struggle to address the
uncertainty in interactions. Actually, objects' functionalities potentially
affect humans' interaction intentions, which reveals what the interaction is.
Meanwhile, the interacting humans and objects exhibit matching geometric
structures, which presents how to interact. In light of this, we propose
harnessing these inherent correlations between interaction counterparts to
mitigate the uncertainty and jointly anticipate the above interaction elements
in 3D space. To achieve this, we present LEMON (LEarning 3D huMan-Object
iNteraction relation), a unified model that mines interaction intentions of the
counterparts and employs curvatures to guide the extraction of geometric
correlations, combining them to anticipate the interaction elements. Besides,
the 3D Interaction Relation dataset (3DIR) is collected to serve as the test
bed for training and evaluation. Extensive experiments demonstrate the
superiority of LEMON over methods estimating each element in isolation.

利用 3D 人物 - 物体交互关系的内在相关性，LEMON 是一个统一模型，通过提取几何相关性并利用曲率来预测交互元素，超越了单独估计每个元素的方法。

LEMON：从 2D 图像中学习 3D 人体物体交互关系

LEMON: Learning 3D Human-Object Interaction Relation from 2D Images

Language-based environment manipulation requires agents to manipulate the
environment following natural language instructions, which is challenging due
to the huge space of the environments. To address this challenge, various
approaches have been proposed in recent work. Although these approaches work
well for their intended environments, they are difficult to generalize across
environments. In this work, we propose LEMON, a general framework for
language-based environment manipulation tasks. Specifically, we first specify a
task-agnostic approach for language-based environment manipulation tasks, which
can deal with various environments using the same generative language model.
Then we propose an execution-guided pre-training strategy to inject prior
knowledge of environments to the language model with a pure synthetic
pre-training corpus. Experimental results on tasks including Alchemy, Scene,
Tangrams, ProPara and Recipes demonstrate the effectiveness of LEMON: it
achieves new state-of-the-art results on four of the tasks, and the
execution-guided pre-training strategy brings remarkable improvements on all
experimental tasks.

本文提出了 LEMON 框架，通过使用生成式语言模型和执行指导的预训练策略，实现面向各种环境的语言操作任务，并在 Alchemy、Scene、Tangrams、ProPara 和 Recipes 任务上取得新的最佳效果。

LEMON：基于语言的环境操作与执行引导的预训练

LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training

Large graphs arise in a number of contexts and understanding their structure
and extracting information from them is an important research area. Early
algorithms on mining communities have focused on the global structure, and
often run in time functional to the size of the entire graph. Nowadays, as we
often explore networks with billions of vertices and find communities of size
hundreds, it is crucial to shift our attention from macroscopic structure to
microscopic structure when dealing with large networks. A growing body of work
has been adopting local expansion methods in order to identify the community
from a few exemplary seed members.
In this paper, we propose a novel approach for finding overlapping
communities called LEMON (Local Expansion via Minimum One Norm). Different from
PageRank-like diffusion methods, LEMON finds the community by seeking a sparse
vector in the span of the local spectra such that the seeds are in its support.
We show that LEMON can achieve the highest detection accuracy among
state-of-the-art proposals. The running time depends on the size of the
community rather than that of the entire graph. The algorithm is easy to
implement, and is highly parallelizable.
Moreover, given that networks are not all similar in nature, a comprehensive
analysis on how the local expansion approach is suited for uncovering
communities in different networks is still lacking. We thoroughly evaluate our
approach using both synthetic and real-world datasets across different domains,
and analyze the empirical variations when applying our method to inherently
different networks in practice. In addition, the heuristics on how the quality
and quantity of the seed set would affect the performance are provided.

本文介绍了一种名为 LEMON (Local Expansion via Minimum One Norm) 的方法，通过局部扩展来发现重叠社区，在人工合成和现实世界数据集上进行了综合分析，并提供了有关种子集的质量和数量如何影响性能的启发式方法。