Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

Unsupervised Online Learning for Robotic Interestingness with Visual Memory

Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require fast deployment with little or no training data. Prior work considers "interestingness" based on data from the same distribution. Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly. To address this problem, we develop a novel translation-invariant visual memory and design a three-stage architecture for long-term, short-term, and online learn…

Robotic Interestingness via Human-Informed Few-Shot Object Detection

Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can adapt to environments and detect interesting scenes quickly, but lack the ability to adapt to human-informed interesting objects. To solve this problem, we introduce a human-interactive framework, AirInteraction, that can detect human-informed objects via few-shot online learning. To reduce the communication bandwidth, we first apply an online unsupervised learning algorithm on the unmanned vehicle for interestingnes…

从舌尖上的遗忘检索查询中无监督建模可记忆性

Unsupervised Memorability Modeling from Tip-of-the-Tongue Retrieval Queries

本文提出首个大规模无监督数据集，通过舌尖上的遗忘检索查询建模视觉内容的可记忆性，并展示了其在回忆生成和ToT检索任务中的优越表现。