Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The paper investigates few-shot fine-grained image classification and emphasizes the importance of phase information for capturing structural relationships. It introduces a plug‑and‑play amplitude‑phase integration (API) module that merges local and global frequency amplitude and phase data to create richer feature descriptors. A new network, PSF‑Net, adaptively fuses phase‑based spatial and frequency information and can be integrated into standard episodic training pipelines, achieving superior performance on five public datasets.
arXiv:2604. 16936v2 Announce Type: replace-cross Abstract: Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC).
arXiv:2610.01807v1 Announce Type: new Abstract: Reliable clinical deployment of deep medical image models is hindered by distribution shifts across scanners, sites, and acquisition protocols. Existin...
arXiv:2607. 00251v1 Announce Type: cross Abstract: While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the importance of accurate phase estimation in recovering sharp image details.
The paper introduces ACF-Net, an optical flow‑guided framework for asymmetric audio‑visual fine‑grained visual categorization (FGVC), addressing challenges where video and audio are not strictly synchronized or matched. ACF-Net comprises Optical Flow‑Guided Motion (OFGM) to capture motion‑sensitive visual cues and suppress background noise, and Asymmetric Cross‑Modal Adaptive Fusion (ACAF) to estimate modality reliability and perform uncertainty‑aware fusion. The authors also present BirdPro, a new bird‑oriented audio‑visual benchmark with 1,919 audio recordings and 11,965 videos across 194 species, and report that ACF‑Net outperforms baselines by 2.97% in fused and 1.92% in mismatched settings.
AudioFuse is a hybrid architecture that jointly learns from spectrograms and raw waveforms to classify phonocardiograms. It combines a wide-and-shallow Vision Transformer for spectral features with a shallow 1D CNN for temporal waveforms, reducing overfitting while capturing complementary information. On the PhysioNet 2016 dataset, AudioFuse achieves a state‑of‑the‑art ROC‑AUC of 0.8608 and shows superior robustness to domain shift on the PASCAL dataset, outperforming both spectrogram‑only and waveform‑only baselines.