[Submitted on 15 Sep 2026]
Title:TAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision
View a PDF of the paper titled TAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision, by Sergey Kurinov (1) and 4 other authors
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Abstract:We present TAPe+ML v3, a compact computer vision system based on TAPe (Theory of Active Perception), a structured representation that encodes relations among perceptual elements before recognition. Instead of operating directly on pixel tensors, the system uses a shared TAPe representation and a modular recognition architecture for image classification, object detection, and instance segmentation.
TAPe+ML v3 combines background and contour processing, local object localization, prototype-based classification, and a coordinator for specialized submodels. Across the reported experiments, it uses fewer than 100,000 parameters. On COCO object detection, it obtains 84.7 mAP50 and 65.3 mAP50-95. On COCO instance segmentation, it obtains 80.7 mask mAP50 and 58.4 mask mAP50-95. In classification experiments, it reaches 92 percent validation accuracy on Imagenette under an identical-training comparison with a raw-pixel baseline, and 89.9 percent Top-1 accuracy on ImageNet-Real. We also evaluate compactness in video scene detection and adaptation under distribution shift in an industrial pilot. The results suggest that shifting part of the modeling burden from network parameters to a structured input representation can support compact multi-task vision systems with reduced data, memory, and compute requirements.
Comments: 39 pages, 4 figures, 11 tables. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
Cite as: arXiv:2609.20869 [cs.CV]
(or arXiv:2609.20869v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.20869
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Alexey Upatov [view email] [v1] Tue, 15 Sep 2026 14:08:43 UTC (2,150 KB)
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