Understanding Activities of Daily Living (ADLs) is a crucial step for
different applications including assistive robots, smart homes, and healthcare.
However, to date, few benchmarks and methods have focused on complex ADLs,
especially those involving multi-person interactions in home environments. In
this paper, we propose a new dataset and benchmark, InteractADL, for
understanding complex ADLs that involve interaction between humans (and
objects). Furthermore, complex ADLs occurring in home environments comprise a
challenging long-tailed distribution due to the rarity of multi-person
interactions, and pose fine-grained visual recognition tasks due to the
presence of semantically and visually similar classes. To address these issues,
we propose a novel method for fine-grained few-shot video classification called
Name Tuning that enables greater semantic separability by learning optimal
class name vectors. We show that Name Tuning can be combined with existing
prompt tuning strategies to learn the entire input text (rather than only
learning the prompt or class names) and demonstrate improved performance for
few-shot classification on InteractADL and 4 other fine-grained visual
classification benchmarks. For transparency and reproducibility, we release our
code at this https URL

提出了一个新的数据集和基准 InteractADL，用于理解涉及人（和物体）之间互动的复杂 ADL，并提出了一种用于细粒度少样本视频分类的新方法 Name Tuning，该方法通过学习优化类别名称向量，使语义可分离，展示了在 InteractADL 和其他四个细粒度视觉分类基准上改进的性能。

日常生活互动活动的少样本分类（InteractADL）

Few-Shot Classification of Interactive Activities of Daily Living  (InteractADL)

Research on group activity recognition mostly leans on the standard
two-stream approach (RGB and Optical Flow) as their input features. Few have
explored explicit pose information, with none using it directly to reason about
the persons interactions. In this paper, we leverage the skeleton information
to learn the interactions between the individuals straight from it. With our
proposed method GIRN, multiple relationship types are inferred from independent
modules, that describe the relations between the body joints pair-by-pair.
Additionally to the joints relations, we also experiment with the previously
unexplored relationship between individuals and relevant objects (e.g.
volleyball). The individuals distinct relations are then merged through an
attention mechanism, that gives more importance to those individuals more
relevant for distinguishing the group activity. We evaluate our method in the
Volleyball dataset, obtaining competitive results to the state-of-the-art. Our
experiments demonstrate the potential of skeleton-based approaches for modeling
multi-person interactions.

本文提出了一个基于骨架信息的新方法 (GIRN)，能够从中直接解释个体之间的交互。该方法将多种关系类型作为独立模块从骨骼信息中推导出来，并利用注意机制将个体不同的关系集成起来。作者在排球数据集上进行了实验，取得了竞争性的效果，提出了基于骨架的方法模拟多人交互的潜力。