  Audio visual segmentation (AVS) aims to segment the sounding objects for each
frame of a given video. To distinguish the sounding objects from silent ones,
both audio-visual semantic correspondence and temporal interaction are
required. The previous method applies multi-frame cross-modal attention to
conduct pixel-level interactions between audio features and visual features of
multiple frames simultaneously, which is both redundant and implicit. In this
paper, we propose an Audio-Queried Transformer architecture, AQFormer, where we
define a set of object queries conditioned on audio information and associate
each of them to particular sounding objects. Explicit object-level semantic
correspondence between audio and visual modalities is established by gathering
object information from visual features with predefined audio queries. Besides,
an Audio-Bridged Temporal Interaction module is proposed to exchange sounding
object-relevant information among multiple frames with the bridge of audio
features. Extensive experiments are conducted on two AVS benchmarks to show
that our method achieves state-of-the-art performances, especially 7.1% M_J and
7.6% M_F gains on the MS3 setting.
