arXiv Computer Vision
From the Drosophila Visual Connectome to General-Purpose Computer Vision
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arXiv Computer Vision
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What Survives on Real Drawings: Active Sampling, Connectome Wiring, and Matched Baselines in Architectural Document Vision
arXiv:2609.24565v2 Announce Type: replace Abstract: A connectome-constrained model of the fly visual system, optimized for motion and then frozen, can be driven over architectural drawings by prescri...
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Structure alone supports efficient visual computation in the Drosophila visual system
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FLYNN: Robust Neural Network for Robot Navigation using Fly Brain Topology
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arXiv Computer Vision
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Fast and Accurate Monomodal 3D High Resolution Deep Registration of Drosophila Larval Brain Volumes
The paper introduces a deep learning pipeline that rapidly and accurately registers 3D high‑resolution Drosophila larval brain volumes to a shared anatomical reference. Unlike traditional methods that require per‑case optimization and minutes per brain, the trained network performs a single forward pass, handling volumes with many more voxels and maintaining high accuracy even as image quality declines. The authors benchmarked their approach against eleven classical and seven learned baselines, achieving a 23‑percentage‑point improvement in landmark‑based mutual information and registering brains one to two orders of magnitude faster.
arXiv Computer Vision
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Active Visual Sampling with a Connectome-Constrained Fly Model for One-Shot Hatch Recognition in Architectural Drawings
arXiv:2609.24565v1 Announce Type: new Abstract: Architectural drawings encode material classes through repeated hatch patterns. We test whether a connectome-constrained fly visual network, pretrained...
arXiv AI
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Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation
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