Newest papers

WildMAE: Structure-Aware Masked Autoencoding for Wildlife Image Reconstruction

Yicheng Zhang, Yujie Jin, Ke Zhang, Jiaxin Wu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28590 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

基因陷阱图像复制的目标图像呈现动物外观,背景混乱,背景混乱和视觉降解的显著变化。 隐藏的自动编码器(MAEs)提供一个标签免费的框架来模拟此类数据,通过重建随机隐藏的图像补丁。 我们介绍了WildMAE,MAE的解码器侧扩展,结合了本地结构意识的改良与隐藏到可见的交叉注意。 一个零门的凝聚性残余面路径引入了前面的两维邻居,而隐藏的交叉注意力允许隐藏的位置从可见位置特征中获取背景信息,而不取代原始的Transformer研究解码器。 相关的培训配方逐渐引入了正常化的pi,Sobeledge的

Machine translation (Meta M2M100); the English original follows.

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-position features without replacing the original Transformer decoder. The associated training recipe progressively introduces normalized-pixel, Sobel-edge, and perceptual objectives. In the original Snapshot Serengeti study, covering 4,744 test images at 75% masking, the reported reconstruction scores were 27.90 versus 24.33 dB PSNR, 0.840 versus 0.780 SSIM, and 0.160 versus 0.320 LPIPS for WildMAE and vanilla MAE, respectively. Additional historical records document favorable average differences under four synthetic degradation conditions and describe component variants. We present the method, experimental observations, and their scope together: the retained metrics are historical protocol-dependent results, rather than a newly reproduced, harmonized comparison or a validation of restoration from genuinely unavailable pixels. WildMAE provides a practical architectural case study for combining global token mixing with spatial and targeted contextual refinement in wildlife masked-image modeling.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Artificial intelligence, Residual, Snapshot (computer storage), Computer vision, Pattern recognition (psychology)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange