PRISM: Progressive Reasoning through Iterative Slot Memory for Vision
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911731419185152 |
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| author | Wang, Ziyu Han, Shuangpeng Zhang, Mengmi |
| author_facet | Wang, Ziyu Han, Shuangpeng Zhang, Mengmi |
| contents | Modern vision models process images in a single feed-forward pass, which limits their ability to recover missing evidence or refine uncertain representations under incomplete observations. Inspired by the iterative nature of human perception, we introduce PRISM (Progressive Reasoning through Iterative Slot Memory), a pyramid vision architecture that reasons over images through iterative refinement. At a high level, PRISM groups visual features into object-centric representations, retrieves relevant patterns from a learned memory, and iteratively refines the representation to resolve ambiguity and recover missing information. This organize-recall-refine process operates recurrently across multiple scales, enabling progressive improvement of visual representations. Across standard vision tasks, including image classification, object detection, and semantic segmentation, PRISM achieves competitive performance while demonstrating improved robustness under incomplete observations such as occlusion. These results suggest that iterative reasoning with structured representations and memory is a promising direction for building more resilient and adaptive vision models. Source code and models will be released. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_30942 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PRISM: Progressive Reasoning through Iterative Slot Memory for Vision Wang, Ziyu Han, Shuangpeng Zhang, Mengmi Computer Vision and Pattern Recognition Modern vision models process images in a single feed-forward pass, which limits their ability to recover missing evidence or refine uncertain representations under incomplete observations. Inspired by the iterative nature of human perception, we introduce PRISM (Progressive Reasoning through Iterative Slot Memory), a pyramid vision architecture that reasons over images through iterative refinement. At a high level, PRISM groups visual features into object-centric representations, retrieves relevant patterns from a learned memory, and iteratively refines the representation to resolve ambiguity and recover missing information. This organize-recall-refine process operates recurrently across multiple scales, enabling progressive improvement of visual representations. Across standard vision tasks, including image classification, object detection, and semantic segmentation, PRISM achieves competitive performance while demonstrating improved robustness under incomplete observations such as occlusion. These results suggest that iterative reasoning with structured representations and memory is a promising direction for building more resilient and adaptive vision models. Source code and models will be released. |
| title | PRISM: Progressive Reasoning through Iterative Slot Memory for Vision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.30942 |