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Binocular Perception Instance Authentication Learning for Few-Shot Visual Recognition
Chaofei Qi1, Peng Li2, Weiyang Lin3
1Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China.
Biomimetics (Basel, Switzerland)
|July 27, 2026
Summary
This study introduces Binocular Perception Instance Authentication Learning (BPIAL), a novel Humanoid-visual Meta-Learning (HvML) approach. BPIAL effectively simulates human binocular vision for superior few-shot visual recognition tasks.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Human few-shot visual recognition surpasses current AI capabilities.
- Existing Machine-visual Meta-Learning (MvML) uses monocular or dual asymmetric architectures.
- No MvML architecture simulates the human binocular visual system (HvML).
Purpose of the Study:
- To propose a novel Humanoid-visual Meta-Learning (HvML) architecture.
- To develop Binocular Perception Instance Authentication Learning (BPIAL) to address MvML limitations.
- To enhance few-shot visual recognition by simulating human binocular vision.
Main Methods:
- Proposed BPIAL, an HvML paradigm.
- Implemented interconnected Binocular Sensation Extraction Modules (BSEM) and Information Authentication Processing Modules (IAPM).
- BSEM simulates visual field extraction, feature processing, and compression; IAPM simulates human logic and reasoning.
Main Results:
- Demonstrated BPIAL feasibility and correctness on five benchmarks.
- Achieved superior and effective performance compared to state-of-the-art methods.
- Alleviated monocular shallowness and dual-branch processing instability inherent in MvML.
Conclusions:
- BPIAL represents a significant advancement in HvML.
- The proposed architecture effectively enhances few-shot visual recognition.
- BPIAL offers a promising direction for developing more human-like AI visual systems.
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