What Survives on Real Drawings: Active Sampling, Connectome Wiring, and Matched Baselines in Architectural Document Vision

arXiv:2609.24565 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-22

Comments: 11 pages, 8 figures, 6 tables. Major revision with a preregistered held-out evaluation, matched graph nulls, temporal sampling controls, real-drawing evaluation, and an ancillary animation

Code: https://github.com/TuragaLab/flyvis

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 18× in this paper · explained in 1 episode
• Benchmark — 15× in this paper · explained in 2 episodes
• Frozen model — 4× in this paper · explained in 3 episodes
• Active Vision — 3× in this paper · explained in 1 episode
• AUC — 2× in this paper · explained in 2 episodes
• Abstention — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Reward Function — 1× in this paper · explained in 2 episodes

Sources

• Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly
• Topological Sensitivity in Connectome-Constrained Neural Networks
• DINOv2: Learning Robust Visual Features without Supervision
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis
• Enhancing Floor Plan Recognition: A Hybrid Mix-Transformer and U-Net Approach for Precise Wall Segmentation

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