## 1 Introduction  
Audio-visual segmentation (AVS), as a multimodal fusion task, intends to identify the positions of sounding objects at the pixel level in a given image [1]. It builds on Sound Source Localization (SSL), which integrates audio and image information to locate sounding objects in an image and mark their regions with a heatmap [2]. Extending SSL, [PERSON] et al. introduced the AVS task and the corresponding AVSbench dataset. This task refines SSL by generating precise masks of sound-emitting objects, enabling accurate localization and boundary delineation. AVS holds significant value in both research and application. On the one hand, AVS integrates the auditory modality with the conventional visual modality to improve the fault tolerance and perception capabilities of the system. On the other hand, AVS technology has broad application potential in domains such as home assistance, smart home control, security monitoring, and healthcare assistance. For example, in security monitoring, fusing visual and acoustic information enables more effective identification and tracking of abnormal noise sources within a given area. This extends the surveillance range, improves monitoring efficiency, and serves as a valuable supplement to fixed video surveillance systems. Due to its practical significance, AVS has attracted substantial research interest and emerged as a prominent topic in the field. Researchers have significantly enhanced the effectiveness of AVS by leveraging the potential of audio-visual data. For instance, [PERSON] et al. proposed a framework that utilizes unlabeled frames, improving marginal performance gains by incorporating motion cues from adjacent frames and semantic information from distant frames [3]. Similarly, [PERSON] et al. introduced an approach that strengthens audio cues through a Bidirectional Audio-Video Decoder (BAVD), enabling continuous interaction between audio and video modalities [4]. [PERSON] et al. developed a hierarchical encoder collaboration module and employed neural architecture search to optimize information interaction [5]. Several studies have focused on the bilateral interaction between visual and auditory modalities. [PERSON] et al. proposed a bidirectional generation framework where visual-to-audio projection reconstructs audio features from object segmentation masks while minimizing reconstruction errors [6]. [PERSON] et al. designed a transformer-based multi-order bilateral architecture, Cooperation of Multi-Order Bilateral Relations (COMBO), which includes a Bilateral Fusion Module (BFM) to precisely align visual and auditory signals [7]. [PERSON] et al. addressed AVS through an Explicit Conditional Multimodal Variational Auto-Encoder (ECMVAE), emphasizing effective representation learning [8]. Other researchers have emphasized enhancing semantic associations in AVS. [PERSON] et al. developed a method based on cross-modal semantic interaction to address ambiguity caused by reliance on visual saliency detection [9]. [PERSON] et al. introduced a text semantic-guided technique that leverages textual cues within visual scenes to strengthen audio semantics [10]. Additionally, [PERSON] et al. proposed the Progressive Confident Masking Attention Network (PMCANet), which employs an attention mechanism to enhance semantic awareness through query token selection [11]. With the advent of the Segment Anything Model (SAM), a universal pre-trained image segmentation model, numerous methods have leveraged this approach for AVS due to its high accuracy and flexible interface [12]. For instance, [PERSON] et al. introduced an efficient audio-visual localization and segmentation framework that integrates visual features with audio features extracted from the SAM's image encoder to construct pixel-level cross-modal representations [13]. [PERSON] et al. proposed a training-free framework, Audio-Language-Referenced SAM 2 (AL-Ref-SAM2), built on SAM2 and a GPT.