  Community researchers have developed a range of advanced audio-visual
segmentation models aimed at improving the quality of sounding objects' masks.
While masks created by these models may initially appear plausible, they
occasionally exhibit anomalies with incorrect grounding logic. We attribute
this to real-world inherent preferences and distributions as a simpler signal
for learning than the complex audio-visual grounding, which leads to the
disregard of important modality information. Generally, the anomalous phenomena
are often complex and cannot be directly observed systematically. In this
study, we made a pioneering effort with the proper synthetic data to categorize
and analyze phenomena as two types "audio priming bias" and "visual prior"
according to the source of anomalies. For audio priming bias, to enhance audio
sensitivity to different intensities and semantics, a perception module
specifically for audio perceives the latent semantic information and
incorporates information into a limited set of queries, namely active queries.
Moreover, the interaction mechanism related to such active queries in the
transformer decoder is customized to adapt to the need for interaction
regulating among audio semantics. For visual prior, multiple contrastive
training strategies are explored to optimize the model by incorporating a
biased branch, without even changing the structure of the model. During
experiments, observation demonstrates the presence and the impact that has been
produced by the biases of the existing model. Finally, through experimental
evaluation of AVS benchmarks, we demonstrate the effectiveness of our methods
in handling both types of biases, achieving competitive performance across all
three subsets.
