Narrowing the Aperture for the Tail: Controllable Spatial Evidence for Long-Tailed Recognition

Multi-expert architectures have emerged as a prominent paradigm for long-tailed visual recognition by distributing specialized classifiers across the class frequency spectrum. However, current frameworks uniformly employ Global Average Pooling (GAP) for spatial feature aggregation, implicitly enforcing an identical spatial observation scale for all classes and experts. In contrast to frequent classes that often benefit from broader contextual integration, rare categories tend to rely more heavily on compact, discriminative local cues. To address this architectural bottleneck, we propose Spatial Moment, a framework that endows controllable multi-expert networks with preference-conditioned evidence apertures. By coupling the hypernetwork preference state with a generalized-mean pooling ladder, our method enables tail-oriented experts to adaptively contract their spatial apertures, concentrating aggregation weights onto local, high-magnitude evidence. To complement this spatial mechanism, we further introduce a bounded one-sided geometric offset derived from within-expert classifier radii to restore radial magnitude statistics canceled by cosine normalization, alongside a class-orthogonal directional regularizer that discourages probability mass from concentrating on competing classes. During inference, experts are aggregated via a parameter-free, decisiveness-aware consensus route that maintains stable ensemble behavior. Extensive evaluations on long-tailed benchmarks and test-prior agnostic scenarios demonstrate that our approach achieves competitive performance against strong multi-expert baselines. Component removals and spatial-flow analyses indicate that adaptive aperture modulation is the primary driver of performance gains, mitigating head-bias classification errors on minority-class samples.