TAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision

arXiv:2609.20869 · cs.CV, eess.IV · Submitted 2026-09-15 · Read on arXiv

cs.CV, eess.IV

Submitted: 2026-09-15

Updated: 2026-09-24

Code: https://github.com/ultralytics/ultralytics

Terminology

• Accuracy — 44× in this paper · explained in 1 episode
• Adaptation — 32× in this paper · explained in 1 episode
• Transformer — 19× in this paper · explained in 2 episodes
• Clustering — 15× in this paper · explained in 2 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Core — 10× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Optical Flow — 8× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Instance Segmentation — 4× in this paper · explained in 3 episodes
• Active Perception — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Edge Deployment — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• RF-DETR: Neural Architecture Search for Real-Time Detection Transformers
• DINOv2: Learning Robust Visual Features without Supervision
• YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
• YOLOv10: Real-Time End-to-End Object Detection
• DINOv3
• Are we done with ImageNet?

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