APRVOS: 1st Place Winner of 5th PVUW MeViS-Audio Track
Abstract
This report presents an Audio-aware Referring Video Object Segmentation (Ref-VOS) pipeline tailored to the MEVIS\_Audio setting, where the referring expression is provided in spoken form rather than as clean text. Compared with a standard Sa2VA-based Ref-VOS pipeline, the proposed system introduces two additional front-end stages: speech transcription and visual existence verification. Specifically, we first employ VibeVoice-ASR to convert long-form spoken input into a structured textual transcript. Since audio-derived queries are inherently noisy and may describe entities that are not visually present in the video, we then introduce an Omni-based judgment module to determine whether the transcribed target can be grounded in the visual content. If the target is judged to be absent, the pipeline terminates early and outputs all-zero masks. Otherwise, the transcript is transformed into a segmentation-oriented prompt and fed into Sa2VA to obtain a coarse mask trajectory over the full video. Importantly, this trajectory is treated as an initial semantic hypothesis rather than a final prediction. On top of it, an agentic refinement layer evaluates query reliability, temporal relevance, anchor quality, and potential error sources, and may invoke SAM3 to improve spatial boundary precision and temporal consistency. The resulting framework explicitly decomposes the MEVIS\_Audio task into audio-to-text conversion, visual existence verification, coarse video segmentation, and agent-guided refinement. Such a staged design is substantially more appropriate for audio-conditioned Ref-VOS than directly sending noisy ASR outputs into a segmentation model.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a robust pipeline for audio-aware referring video object segmentation that effectively addresses the challenges posed by noisy audio inputs through a staged processing approach. This work significantly advances the state of the art in Ref-VOS by explicitly modeling uncertainties from both speech recognition and visual grounding, thereby enhancing the accuracy and reliability of segmentation outcomes.
Methodology
The proposed methodology introduces a novel pipeline for Audio-aware Referring Video Object Segmentation (Ref-VOS) that effectively addresses the unique challenges posed by audio-derived queries. By incorporating stages for speech transcription and visual existence verification, the authors create a robust framework that minimizes the impact of ASR noise and enhances segmentation accuracy. The staged design allows for a clear separation of tasks, which is a significant improvement over traditional models that treat audio input as a straightforward text query. This decomposition into distinct stages not only clarifies the processing flow but also allows for targeted improvements at each step, showcasing a thoughtful approach to the problem.
Results & evaluation
The experimental evaluation includes a well-structured ablation study that demonstrates the incremental benefits of each component of the proposed pipeline. The results indicate that the addition of the visual existence judgment stage significantly enhances performance, highlighting the importance of addressing ASR errors before segmentation. The reported scores reflect a competitive performance in the MEVIS_Audio challenge, providing evidence of the effectiveness of the proposed method. However, the paper could benefit from a more extensive evaluation against baseline models and a broader set of datasets to further validate the approach.
Reproducibility
The paper lacks detailed implementation specifics that would facilitate reproducibility. While the methodology is clearly outlined, the absence of code or supplementary materials makes it difficult for other researchers to replicate the results. Providing access to the model architecture, training procedures, and datasets used would greatly enhance the reproducibility of the findings.
Limitations
One limitation of the proposed approach is its reliance on the quality of the ASR system, VibeVoice-ASR. If the ASR output is significantly flawed, it could lead to erroneous visual existence judgments and subsequent segmentation errors. Additionally, the complexity of the pipeline may introduce challenges in real-time applications, where speed is critical. The paper also does not discuss the potential impact of varying audio qualities or accents on ASR performance, which could affect the generalizability of the approach.
Broader impact
The proposed framework has significant implications for various applications, including video surveillance, content retrieval, and interactive media where audio queries are prevalent. By improving the robustness of video segmentation in the presence of noisy audio inputs, this work could enhance user experiences in multimedia applications and contribute to advancements in human-computer interaction. The methodology also opens avenues for further research in multimodal learning, particularly in integrating audio and visual data for more complex tasks. The main contribution of this paper is the introduction of a robust pipeline for audio-aware referring video object segmentation that effectively addresses the challenges posed by noisy audio inputs through a staged processing approach. This work significantly advances the state of the art in Ref-VOS by explicitly modeling uncertainties from both speech recognition and visual grounding, thereby enhancing the accuracy and reliability of segmentation outcomes.
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