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ManiDream: Manifold Completion via Prototype-Anchored Dreaming for Long-Tailed Visual Recognition
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AI
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王
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DOI：10.1007/s11263-026-02988-9.png)
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Abstract
En 中文
Long-tailed visual recognition is fundamentally constrained by the geometric sparsity of tail-class feature manifolds, where data scarcity leads to vacuous representation spaces and collapsed decision boundaries. While existing discriminative approaches such as re-balancing and decoupling mitigate bias, they operate passively on observed samples and fail to replenish the missing semantic information in the tail. Drawing inspiration from the brain’s complementary learning systems, specifically the interplay between predictive coding during wakefulness and generative consolidation during sleep, we propose ManiDream, a bio-inspired framework for active feature manifold completion. The framework operates in a rhythmic two-phase cycle. First, in the Online Perception phase, we introduce an Attentive Predictive Estimator (APE) that detects semantic surprise by measuring attention-weighted reconstruction errors. This mechanism enables the model to precisely pinpoint and store hard examples with high epistemic uncertainty. Second, in the Offline Consolidation phase, we devise a Prototype-Anchored Dreaming (PAD) mechanism. PAD employs a residual generative model to actively synthesize high-fidelity pseudo-features within the geometric vicinity of real prototypes, thereby filling the manifold voids without semantic drift. Crucially, to address the feature energy decay inherent in generative augmentation, we introduce a physics-inspired Metacognitive Norm Rectification strategy that preserves feature-norm consistency and maintains effective gradient magnitudes during classifier optimization. Extensive experiments across five benchmarks, including CIFAR-100-LT, CIFAR-10-LT, ImageNet-LT, iNaturalist 2018, and Places-LT, demonstrate that ManiDream achieves competitive or state-of-the-art performance on standard long-tailed recognition protocols and improves robustness across most distribution-shift settings, supporting the effectiveness of active manifold completion.
Keywords:
Long-tailed recognition
Predictive coding
Feature generation
Manifold completion
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