On Locality and Length Generalization in Visual Reasoning
This makes human vision distinctly different from most popular computer vision models in use today, which input images globally and in a single shot. A natural question therefore is whether local, sequential vision models may provide any fundamental computational benefits in addition to being biologically more plausible than global models. In this work, we investigate this question from the perspective of visual state tracking and length generalization.
- ▪This makes human vision distinctly different from most popular computer vision models in use today, which input images globally and in a single shot.
- ▪A natural question therefore is whether local, sequential vision models may provide any fundamental computational benefits in addition to being biologically more plausible than global models.
- ▪In this work, we investigate this question from the perspective of visual state tracking and length generalization.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.09061 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
| Last seen | 2026-07-13T04:20:37.625Z |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2607.09061 (cs) [Submitted on 10 Jul 2026] Title:On Locality and Length Generalization in Visual Reasoning Authors:Pulkit Madan, Sanjay Haresh, Reza Ebrahimi, Sunny Panchal, Apratim Bhattacharyya, Roland Memisevic View a PDF of the paper titled On Locality and Length Generalization in Visual Reasoning, by Pulkit Madan and 5 other authors View PDF HTML (experimental) Abstract:A striking feature of the human visual system is that it ingests visual information through a series of local foveated glimpses, rather than a single global computation. This makes human vision distinctly different from most popular computer vision models in use today, which input images globally and in a single shot.
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