Paper recorded by Signals 4 on 2026-10-20 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-20 on arXiv · recorded by Signals 4 on 2026-10-21
Category: cs.LG · 机器学习 · first seen 2026-10-21
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-atten