AI RESEARCH
Frequency-Adaptive Discrete Cosine-ViT-ResNet Architecture for Sparse-Data Vision
arXiv CS.CV
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ArXi:2505.22701v2 Announce Type: replace A major challenge in rare animal image classification is the scarcity of data, as many species usually have only a small number of labeled samples. To address this challenge, we designed a hybrid deep-learning framework comprising a novel adaptive DCT preprocessing module, ViT-B16 and ResNet50 backbones, and a Bayesian linear classification head. To our knowledge, we are the first to Our network first captures image frequency-domain cues via this adaptive DCT partitioning.