  Audio-visual saliency prediction can draw support from diverse modality
complements, but further performance enhancement is still challenged by
customized architectures as well as task-specific loss functions. In recent
studies, denoising diffusion models have shown more promising in unifying task
frameworks owing to their inherent ability of generalization. Following this
motivation, a novel Diffusion architecture for generalized audio-visual
Saliency prediction (DiffSal) is proposed in this work, which formulates the
prediction problem as a conditional generative task of the saliency map by
utilizing input audio and video as the conditions. Based on the spatio-temporal
audio-visual features, an extra network Saliency-UNet is designed to perform
multi-modal attention modulation for progressive refinement of the ground-truth
saliency map from the noisy map. Extensive experiments demonstrate that the
proposed DiffSal can achieve excellent performance across six challenging
audio-visual benchmarks, with an average relative improvement of 6.3\% over the
previous state-of-the-art results by six metrics.
