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

• URL: http://arxiv.org/abs/2405.06185v1
• Date: Fri, 10 May 2024 01:56:39 GMT
• Title: Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection
• Authors: Koji Takeda, Kanji Tanaka, Yoshimasa Nakamura, Asako Kanezaki,
• Abstract summary: In everyday indoor navigation, robots often needto detect non-distinctive small-change objects. Existing techniques rely on high-quality class-specific object priors to regularize a change detector model. In this study, we explore the concept of degree-of-ill-posedness (DoI) to improve both passive and activevision.
• Score: 8.977792536037956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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