Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
Zane Durante, Robathan Harries, Edward Vendrow, Zelun Luo, Yuta, Kyuragi, Kazuki Kozuka, Li Fei-Fei, Ehsan Adeli

TL;DR
This paper introduces InteractADL, a new dataset and benchmark for complex multi-person activity recognition in home environments, and proposes Name Tuning, a novel few-shot video classification method that improves semantic separability.
Contribution
The paper presents a new dataset and benchmark for complex ADLs involving multi-person interactions, and introduces Name Tuning, a method enhancing few-shot classification performance.
Findings
Name Tuning improves few-shot classification accuracy.
InteractADL benchmark enables better evaluation of multi-person ADL recognition.
Method outperforms existing strategies on multiple benchmarks.
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…
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Taxonomy
TopicsOnline Learning and Analytics · Context-Aware Activity Recognition Systems
