OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference

• URL: http://arxiv.org/abs/2507.02929v1
• Date: Thu, 26 Jun 2025 05:57:06 GMT
• Title: OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference
• Authors: Won-Seok Choi, Dong-Sig Han, Suhyung Choi, Hyeonseo Yang, Byoung-Tak Zhang,
• Abstract summary: We present the Object-Based Sub-Environment Recognition (OBSER) framework, a novel Bayesian framework that infers three fundamental relationships between sub-environments and their constituent objects.<n>We validate the proposed framework by introducing the ($epsilon,delta$) statistically separable (EDS) function which indicates the alignment of the representation.
• Score: 18.514809279438914
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present the Object-Based Sub-Environment Recognition (OBSER) framework, a novel Bayesian framework that infers three fundamental relationships between sub-environments and their constituent objects. In the OBSER framework, metric and self-supervised learning models estimate the object distributions of sub-environments on the latent space to compute these measures. Both theoretically and empirically, we validate the proposed framework by introducing the ($\epsilon,\delta$) statistically separable (EDS) function which indicates the alignment of the representation. Our framework reliably performs inference in open-world and photorealistic environments and outperforms scene-based methods in chained retrieval tasks. The OBSER framework enables zero-shot recognition of environments to achieve autonomous environment understanding.

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