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VICAL framework improves long-tailed visual recognition by reducing prediction variance

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced VICAL, a novel framework designed to enhance long-tailed visual recognition. Unlike previous methods that focused on maximizing expert diversity in multi-expert models, VICAL prioritizes variance reduction. The framework incorporates Self-Consistency Learning to smooth the loss landscape and mitigate overfitting, particularly for tail classes, and Deep Ensemble Distillation to foster cross-expert semantic agreement. Experiments on datasets like CIFAR-LT, ImageNet-LT, and iNaturalist 2018 demonstrate VICAL's superior performance compared to existing state-of-the-art techniques. AI

IMPACT This research offers a new approach to improving AI models' performance on datasets with imbalanced class distributions.

RANK_REASON The cluster contains an academic paper detailing a new method for visual recognition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VICAL framework improves long-tailed visual recognition by reducing prediction variance

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The cluster contains an academic paper detailing a new method for visual recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiangang Zhu, Zheng Wang, Bin Zhu, Yi-Ping Phoebe Chen, Jingjing Chen ·

    VICAL: Vicinal Consistency Alignment for Long-Tailed Visual Recognition

    arXiv:2609.04948v1 Announce Type: cross Abstract: Multi-expert models have become the dominant paradigm for long-tailed learning, largely attributed to their presumed ability to benefit from expert diversity. However, we revisit this central assumption and reveal that diversity i…

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