Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection

TL;DR

This paper introduces a zero-shot approach to estimate the degree of ill-posedness in change detection for small, semantically nondistinctive objects in indoor navigation, enhancing existing models without requiring extensive labeled data.

Contribution

It proposes a novel zero-shot degree-of-ill-posedness estimation method using self-supervised learning and oversegmentation cues, improving change detection for small objects in diverse datasets.

Findings

Boosts state-of-the-art change detection models

Shows stable improvements across datasets

Effective in real-world indoor navigation scenarios

Abstract

In everyday indoor navigation, robots often needto detect non-distinctive small-change objects (e.g., stationery,lost items, and junk, etc.) to maintain domain knowledge. Thisis most relevant to ground-view change detection (GVCD), a recently emerging research area in the field of computer vision.However, these existing techniques rely on high-quality class-specific object priors to regularize a change detector modelthat cannot be applied to semantically nondistinctive smallobjects. To address ill-posedness, in this study, we explorethe concept of degree-of-ill-posedness (DoI) from the newperspective of GVCD, aiming to improve both passive and activevision. This novel DoI problem is highly domain-dependent,and manually collecting fine-grained annotated training datais expensive. To regularize this problem, we apply the conceptof self-supervised learning to achieve efficient DoI estimationscheme and investigate its generalization to diverse datasets.Specifically, we tackle the challenging issue of obtaining self-supervision cues for semantically non-distinctive unseen smallobjects and show that novel "oversegmentation cues" from openvocabulary semantic segmentation can be effectively exploited.When applied to diverse real datasets, the proposed DoI modelcan boost state-of-the-art change detection models, and it showsstable and consistent improvements when evaluated on real-world datasets.