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

Koji Takeda, Kanji Tanaka, Yoshimasa Nakamura, Asako Kanezaki

TL;DR

This work tackles the ill-posedness of detecting small, semantically nondistinctive object changes in ground-view scenes by introducing Degree of Ill-posedness (DoI) for GVCD and a zero-shot DoI estimation framework. It combines a base change detector with an object-search pipeline that leverages large multimodal models (including SAM, Grounding DINO, LLaVA with Alpha CLIP) to generate open-vocabulary object masks and linguistic labels, then integrates these with a DoI-based decision rule to refine changes. The key contribution is a novel, training-free (zero-shot) DoI estimator that improves state-of-the-art change detectors across diverse real-world datasets, particularly in cluttered environments, while highlighting limitations in regions where the baseline detector already underperforms. The results demonstrate a practical path toward active vision: using DoI to trigger targeted inspections and plan next-best-views, potentially enhancing robotic navigation and object-tracking capabilities in indoor environments.

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.

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

TL;DR

This work tackles the ill-posedness of detecting small, semantically nondistinctive object changes in ground-view scenes by introducing Degree of Ill-posedness (DoI) for GVCD and a zero-shot DoI estimation framework. It combines a base change detector with an object-search pipeline that leverages large multimodal models (including SAM, Grounding DINO, LLaVA with Alpha CLIP) to generate open-vocabulary object masks and linguistic labels, then integrates these with a DoI-based decision rule to refine changes. The key contribution is a novel, training-free (zero-shot) DoI estimator that improves state-of-the-art change detectors across diverse real-world datasets, particularly in cluttered environments, while highlighting limitations in regions where the baseline detector already underperforms. The results demonstrate a practical path toward active vision: using DoI to trigger targeted inspections and plan next-best-views, potentially enhancing robotic navigation and object-tracking capabilities in indoor environments.

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.
Paper Structure (12 sections, 4 equations, 7 figures, 1 table)

This paper contains 12 sections, 4 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Approach
  4. Base Change Detection Module
  5. Object Search Module
  6. Integration Module
  7. Experiments
  8. Dataset
  9. Settings
  10. Comparing Methods
  11. Result
  12. Conclusion

Figures (7)

  • Figure 1: Degree of Ill-posedness (DoI) in small object change detection. If DoI is high, robot should temporarily suspend its own navigation task and and trigger an active change-detection task in which it approaches and closely inspects potential small object changes.
  • Figure 2: Proposed change detection framework. First, a binary change mask is obtained from two images, a reference image and a live image, in the Base change detection module. Next, the object search module get object mask for reference image and live image by utilizing large multimodal model and open vocabulary segmentation. Finally, the final change detection result is calculated while evaluating the DoI using the Integration module.
  • Figure 3: Overview of synthetic training set creation. Randomly select an image from the reference images and paste a COCO object to obtain a reference image, pseudo live image, and pseudo ground truth mask.
  • Figure 4: Results of large multimodal model. (a) live image, (b) reference image, (c) ground truth mask, (d) predicted object label
  • Figure 5: Differential two-wheeled robot equipped with a Realsense D455, 2D LiDAR, IMU was used to collect dataset.
  • ...and 2 more figures