## 1 Introduction
This framework utilizes GroundingDINO for single-frame object recognition, GPT-4 for selecting reference information, and SAM2 for segmenting recognized objects across video sequences [14]. Similarly, [PERSON] et al. introduced Modality Correspondence Alignment (MoCA), an AVS approach that seamlessly integrates pre-trained foundational models such as DINO, the SAM, and ImageBind. Leveraging the complementary knowledge of these models, MoCA optimizes their synergistic application to achieve multimodal correlation [15]. In pursuit of higher accuracy, conventional AVS models often employ complex architectures, resulting in insufficient efficiency. To address this issue, [PERSON] et al. introduced SAMA-AVS, a lightweight model that utilizes the pre-trained SAM for AVS tasks. By integrating a limited number of trainable parameters and adapters, this approach effectively achieves audio-visual fusion and interaction during the encoding phase [16]. Similarly, [PERSON] et al. developed Segment Audio-Visual Easy Way (SAVE), a lightweight AVS model that fine-tunes transformer-based image encoders and employs a residual audio encoder to generate sparse audio feature hints, facilitating effective audio-visual fusion [17]. [PERSON] et al. proposed a Vision Transformer (ViT)-based lightweight AVS framework that injects a minimal number of trainable parameters into each ViT layer for fine-tuning, adapting pre-trained ViTs for audio-visual tasks [18]. Additionally, [PERSON] et al. introduced EachPerforms Its Functions (PIF), an efficient AVS method that divides the task into two stages: relevance learning and segmentation refinement. This approach leverages deep features for cross-modal interaction and shallow features to enhance segmentation results [19]. However, despite these advancements, the aforementioned algorithms still incorporate a significant number of parameters, and the SAM exhibits high computational complexity and extensive processing time. These limitations pose challenges for deployment on embedded platforms, underscoring the need for further improvements in model efficiency. In addition to the challenge of model lightweighting, general AVS methods often fail to integrate object tracking and neglect the mis-segmentation of unvoiced objects caused by environmental noise. These limitations remain critical barriers to practical application, as illustrated in Figure 1.  
To address the above issues, this study proposes a lightweight AVS framework that incorporates an audio-guided space-time memory network. The overall framework diagram of the proposed architecture is shown as Figure 2. The key contributions of this study are divided into the following two aspects. * To achieve lightweight audio-visual segmentation, this research proposes an efficient audio-visual mask generator leveraging pre-trained lightweight models, Fastsam and Wav2 CLIP. Additionally, a mask-scoring mechanism is designed to optimize the mask, thereby enhancing the accuracy and precision of target segmentation;
* In addition to the audio-visual fusion mask, to enable continuous tracking of the vocal target guided by the fusion mask, this research proposes a video object tracking network based on a space-time memory network (STM-Net). The audio feature extraction and fusion module within the network detects changes in the audio signal and transmits the output information to the upsampling module of the mask decoder to guide the generation of the final segmentation mask, thereby suppressing the interference of environmental noise. Figure 1: In real-world scenarios, the phenomenon of the mis-segmentation of unvoiced objects often occurs as a result of environmental noise. When conducting object segmentation tasks, especially for those targets that are unvoiced, the presence of environmental noise can severely disrupt the normal operation of the segmentation model.