Frequency-Adaptive Discrete Cosine-ViT-ResNet Architecture for Sparse-Data Vision

• URL: http://arxiv.org/abs/2505.22701v1
• Date: Wed, 28 May 2025 17:39:58 GMT
• Title: Frequency-Adaptive Discrete Cosine-ViT-ResNet Architecture for Sparse-Data Vision
• Abstract summary: We introduce an adaptive frequency-domain selection mechanism that learns optimal low-, mid-, and high-frequency boundaries.<n>On our self-built 50-class wildlife dataset, this approach outperforms conventional CNN and fixed-band DCT pipelines.
• Score: 7.0134322436635275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 introduce an adaptive frequency-domain selection mechanism that learns optimal low-, mid-, and high-frequency boundaries suited to the subsequent backbones. Our network first captures image frequency-domain cues via this adaptive DCT partitioning. The adaptively filtered frequency features are then fed into ViT-B16 to model global contextual relationships, while ResNet50 concurrently extracts local, multi-scale spatial representations from the original image. A cross-level fusion strategy seamlessly integrates these frequency- and spatial-domain embeddings, and the fused features are passed through a Bayesian linear classifier to output the final category predictions. On our self-built 50-class wildlife dataset, this approach outperforms conventional CNN and fixed-band DCT pipelines, achieving state-of-the-art accuracy under extreme sample scarcity.

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