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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.13883 |
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| _version_ | 1866913035252137984 |
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| author | Born, Frieda Neuhäuser, Tom Muttenthaler, Lukas Roads, Brett D. Spitzer, Bernhard Lampinen, Andrew K. Jones, Matt Müller, Klaus-Robert Mozer, Michael C. |
| author_facet | Born, Frieda Neuhäuser, Tom Muttenthaler, Lukas Roads, Brett D. Spitzer, Bernhard Lampinen, Andrew K. Jones, Matt Müller, Klaus-Robert Mozer, Michael C. |
| contents | Modern machine learning models typically represent inputs as fixed points in a high-dimensional embedding space. While this approach has been proven powerful for a wide range of downstream tasks, it fundamentally differs from the way humans process information. Because humans are constantly adapting to their environment, they represent objects and their relationships in a highly context-sensitive manner. To address this gap, we propose a method for context-sensitive similarity computation from neural network embeddings, applied to modeling a triplet odd-one-out task with an anchor image serving as simultaneous context. Modeling context enables us to achieve up to a 15% improvement in odd-one-out accuracy over a context-insensitive model. We find that this improvement is consistent across both original and "human-aligned" vision foundation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13883 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context Sensitivity Improves Human-Machine Visual Alignment Born, Frieda Neuhäuser, Tom Muttenthaler, Lukas Roads, Brett D. Spitzer, Bernhard Lampinen, Andrew K. Jones, Matt Müller, Klaus-Robert Mozer, Michael C. Computer Vision and Pattern Recognition Machine Learning Modern machine learning models typically represent inputs as fixed points in a high-dimensional embedding space. While this approach has been proven powerful for a wide range of downstream tasks, it fundamentally differs from the way humans process information. Because humans are constantly adapting to their environment, they represent objects and their relationships in a highly context-sensitive manner. To address this gap, we propose a method for context-sensitive similarity computation from neural network embeddings, applied to modeling a triplet odd-one-out task with an anchor image serving as simultaneous context. Modeling context enables us to achieve up to a 15% improvement in odd-one-out accuracy over a context-insensitive model. We find that this improvement is consistent across both original and "human-aligned" vision foundation models. |
| title | Context Sensitivity Improves Human-Machine Visual Alignment |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2604.13883 |