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Adaptive Hebbian Routing enhances few-shot Vision Transformer performance

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an Adaptive Hebbian Routing method for few-shot Vision Transformers to improve image recognition from limited data. This approach uses a lightweight MLP router to dynamically control Hebbian memory contributions, update strengths, and retention of previous memory. Experiments on various backbones and datasets demonstrated that adaptive variants enhance performance and reduce inference time compared to fixed Hebbian methods, showing the benefit of adaptive plasticity and memory activation. AI

IMPACT Improves efficiency and accuracy of image recognition models trained on limited datasets.

RANK_REASON The cluster contains a research paper detailing a new method for few-shot learning in computer vision.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Adaptive Hebbian Routing enhances few-shot Vision Transformer performance

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Mohammed Yusuf Mujawar, Noorbakhsh Amiri Golilarz ·

    Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning

    arXiv:2606.24756v1 Announce Type: new Abstract: Few-shot image recognition requires models to adapt to new classes from a small labeled support set. Hebbian fast-weight memory can provide temporary associative information during an episode, but fixed memory behavior may not be ap…

  2. arXiv cs.CV TIER_1 English(EN) · Noorbakhsh Amiri Golilarz ·

    Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning

    Few-shot image recognition requires models to adapt to new classes from a small labeled support set. Hebbian fast-weight memory can provide temporary associative information during an episode, but fixed memory behavior may not be appropriate for every few-shot task. In this work,…

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