WildMAE: Structure-Aware Masked Autoencoding for Wildlife Image Reconstruction

Yicheng Zhang, Yujie Jin, Ke Zhang, Jiaxin Wu · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28590 · open-access PDF

Abstract

Camera-trap imagery presents substantial variation in animal appearance, illumination, background clutter, and visual degradation. Masked autoencoders (MAEs) offer a label-free framework for modeling such data through reconstruction of randomly hidden image patches. We present WildMAE, a decoder-side extension of MAE that combines local structure-aware refinement with masked-to-visible cross-attention. A zero-gated convolutional residual path introduces a two-dimensional neighborhood prior, while gated cross-attention allows masked positions to retrieve contextual information from visible-posi

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