Sergey Kurinov

The work presents TAPe+ML v3, which replaces raw pixel tensors with a structured TAPe (Theory of Active Perception) representation and combines background, pointer, and prototype-clustering submodels under a coordinator; with fewer than 100,000 parameters it reports 84.7 mAP50 and 65.3 mAP50-95 on COCO detection, 80.7 mask mAP50 and 58.4 mask mAP50-95 on COCO instance segmentation, 88.1% ImageNet-1k Top-1 and 89.9% ImageNet-Real, 92% versus 47% on Imagenette under identical training with TAPe versus raw pixels, and an industrial ore-blockage pilot where backbone adaptation outperforms head-only adaptation.

The work presents TAPe+ML v3, which replaces raw pixel tensors with a structured TAPe (Theory of Active Perception) representation and combines background, pointer, and prototype-clustering submodels under a coordinator; with fewer than 100,000 parameters it reports 84.7 mAP50 and 65.3 mAP50-95 on COCO detection, 80.7 mask mAP50 and 58.4 mask mAP50-95 on COCO instance segmentation, 88.1% ImageNet-1k Top-1 and 89.9% ImageNet-Real, 92% versus 47% on Imagenette under identical training with TAPe versus raw pixels, and an industrial ore-blockage pilot where backbone adaptation outperforms head-only adaptation.