OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913926104481792 |
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| author | Choi, Won-Seok Han, Dong-Sig Choi, Suhyung Yang, Hyeonseo Zhang, Byoung-Tak |
| author_facet | Choi, Won-Seok Han, Dong-Sig Choi, Suhyung Yang, Hyeonseo Zhang, Byoung-Tak |
| contents | 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 ($ε,δ$) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Choi, Won-Seok Han, Dong-Sig Choi, Suhyung Yang, Hyeonseo Zhang, Byoung-Tak Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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 ($ε,δ$) 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. |
| title | OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.02929 |