mlsanityData to 14 Oct 2026

Fully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection

arXiv 2607.05148 ↗First posted 6 Jul 2026cs.CVcited by 0

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Abstract (arXiv)

Existing multispectral detectors are limited by discrete spectral processing, a scale-dependent shift in the relative reliability of spectral and spatial cues across pyramid levels, and the lack of explicit rotation-equivariant geometric priors for arbitrarily oriented objects. To tackle these limitations, we propose FressDet, a fully rotation-equivariant spectral-spatial learning framework for multispectral object detection, capable of capturing the continuous, ordered nature of spectral structure and enabling reliable spectral-spatial fusion across pyramid levels under arbitrary in-plane rotations. FressDet integrates three complementary components. Spectral Implicit Warp (SpeIW) enables query-based spectral resampling via a coordinate-conditioned implicit field, yielding a monotone, order-preserving warp. Rotation-Equivariant Consistency Weighting (ReCoW) adaptively fuses spectral and spatial branches based on branch reliability, reinforcing informative cues while suppressing noise across pyramid levels. The oriented-aware head exploits group-indexed features to stably predict oriented objects without parameter replication. Taken together, FressDet learns more discriminative and robust spectral-spatial representations even under rotational perturbations. By achieving state-of-the-art performance with 93% fewer parameters on five public benchmarks, FressDet demonstrates its effectiveness and generalizability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 94 of its 105 references that are in the map. First posted on arXiv 6 Jul 2026.

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