Abstract
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.
Abstract (translated)
理解日常活动活动（ADLs）对于诸如辅助机器人、智能家居和医疗设备等各个应用领域来说至关重要。然而，到目前为止，很少有基准和方法关注复杂ADL，尤其是涉及多人在家庭环境中的交互。在本文中，我们提出了一个新的数据集和基准，称为交互ADL，以了解复杂ADL中的人（及其与物体）之间的交互。此外，家庭环境中的复杂ADL由于多个人类交互的稀有性，构成了一个具有挑战性的长尾分布，并提出了细粒度的视觉识别任务。为了应对这些问题，我们提出了一个名为Name Tuning的新方法，一种通过学习最优分类名称向量来提高语义分离的细粒度视觉分类方法。我们证明了Name Tuning可以与现有的提示调整策略相结合，以学习整个输入文本（而不仅仅是提示或分类名称），并在InteractADL和四个其他细粒度视觉分类基准上取得了较好的性能。为了保证透明度和可重复性，我们将代码发布在此处：https://www.example.com/。
URL
https://arxiv.org/abs/2406.01662
