Feature pyramid attention network for audio-visual scene classification

Abstract

Audio-visual scene classification (AVSC) poses a formidable challenge owing to the intricate spatial-temporal relationships exhibited by audio-visual signals, coupled with the complex spatial patterns of objects and textures found in visual images. The focus of recent studies has predominantly revolved around extracting features from diverse neural network structures, inadvertently neglecting the acquisition of semantically meaningful regions and crucial components within audio-visual data. The authors present a feature pyramid attention network (FPANet) for audio-visual scene understanding, which extracts semantically significant characteristics from audio-visual data. The authors’ approach builds multi-scale hierarchical features of sound spectrograms and visual images using a feature pyramid representation and localises the semantically relevant regions with a feature pyramid attention module (FPAM). A dimension alignment (DA) strategy is employed to align feature maps from multiple layers, a pyramid spatial attention (PSA) to spatially locate essential regions, and a pyramid channel attention (PCA) to pinpoint significant temporal frames. Experiments on visual scene classification (VSC), audio scene classification (ASC), and AVSC tasks demonstrate that FPANet achieves performance on par with state-of-the-art (SOTA) approaches, with a 95.9 F1-score on the ADVANCE dataset and a relative improvement of 28.8%. Visualisation results show that FPANet can prioritise semantically meaningful areas in audio-visual signals.