arXiv AI
Sep 4

The impact of phase information for few-shot fine-grained image classification

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.

By Ruiling Liu, Linyue Zhang, Wenyi Zeng, Jiamiao Lu, Weichuang Zhang, Changming Sun, Zejun Zhang, Xiao Zhao
arXiv Computer Vision
Aug 27

Asymmetric Cross-Modal Fine-Grained Visual Categorization: ACF-Net and the BirdPro Benchmark

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.

By Bohan Deng, Shuo Ye, Zitong Yu
arXiv AI
Sep 10

AudioFuse: Unified Spectral-Temporal Learning via a Hybrid ViT-1D CNN Architecture for Robust Phonocardiogram Classification

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.

By Md. Saiful Bari Siddiqui, Utsab Saha