Self-supervised audio-visual source localization aims to locate sound-source
objects in video frames without extra annotations. Recent methods often
approach this goal with the help of contrastive learning, which assumes only
the audio and visual contents from the same video are positive samples for each
other. However, this assumption would suffer from false negative samples in
real-world training. For example, for an audio sample, treating the frames from
the same audio class as negative samples may mislead the model and therefore
harm the learned representations e.g., the audio of a siren wailing may
reasonably correspond to the ambulances in multiple images). Based on this
observation, we propose a new learning strategy named False Negative Aware
Contrastive (FNAC) to mitigate the problem of misleading the training with such
false negative samples. Specifically, we utilize the intra-modal similarities
to identify potentially similar samples and construct corresponding adjacency
matrices to guide contrastive learning. Further, we propose to strengthen the
role of true negative samples by explicitly leveraging the visual features of
sound sources to facilitate the differentiation of authentic sounding source
regions. FNAC achieves state-of-the-art performances on Flickr-SoundNet,
VGG-Sound, and AVSBench, which demonstrates the effectiveness of our method in
mitigating the false negative issue. The code is available at
\url{https://github.com/OpenNLPLab/FNAC_AVL}.Comment: CVPR202