  Sound source localization in visual scenes aims to localize objects emitting
the sound in a given image. Recent works showing impressive localization
performance typically rely on the contrastive learning framework. However, the
random sampling of negatives, as commonly adopted in these methods, can result
in misalignment between audio and visual features and thus inducing ambiguity
in localization. In this paper, instead of following previous literature, we
propose Self-Supervised Predictive Learning (SSPL), a negative-free method for
sound localization via explicit positive mining. Specifically, we first devise
a three-stream network to elegantly associate sound source with two augmented
views of one corresponding video frame, leading to semantically coherent
similarities between audio and visual features. Second, we introduce a novel
predictive coding module for audio-visual feature alignment. Such a module
assists SSPL to focus on target objects in a progressive manner and effectively
lowers the positive-pair learning difficulty. Experiments show surprising
results that SSPL outperforms the state-of-the-art approach on two standard
sound localization benchmarks. In particular, SSPL achieves significant
improvements of 8.6% cIoU and 3.4% AUC on SoundNet-Flickr compared to the
previous best. Code is available at: https://github.com/zjsong/SSPL.
