TAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision
TAPe+ML v3 reaches 84.7 COCO detection mAP50 with under 100,000 parameters via a structured Theory of Active Perception representation.
TAPe+ML v3 is a compact multi-task computer vision system that encodes relations among perceptual elements in a shared TAPe representation instead of raw pixel tensors, combining contour processing, localization, prototype classification, and a coordinator over submodels. Using fewer than 100,000 parameters, it scores 84.7 mAP50 on COCO detection, 80.7 mask mAP50 on COCO segmentation, and 89.9 percent top-1 on ImageNet-Real. Compactness and distribution-shift adaptation were additionally evaluated in an industrial pilot.
- Under 100,000 parameters across classification, detection, and segmentation tasks
- 84.7 mAP50 COCO detection; 80.7 mask mAP50 COCO segmentation
- Structured representation shifts modeling burden away from network parameters
From the original article
1 min read at huggingface.co
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.
By Sergey Kurinov, Alexey Upatov · huggingface.co
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