arxiv: 2604.18665 · v1 · submitted 2026-04-20 · 💻 cs.SD

Recognition: unknown

APRVOS: 1st Place Winner of 5th PVUW MeViS-Audio Track

Deshui Miao , Yameng Gu , Chao Yang , Xin Li , Haijun Zhang , Ming-Hsuan Yang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:25 UTC · model grok-4.3

classification 💻 cs.SD

keywords audio-aware referring video object segmentationMeViS-Audiospeech transcriptionvisual existence verificationagentic refinementSa2VASAM3

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The pith

A staged pipeline with transcription, visual verification, coarse segmentation, and agentic refinement handles noisy spoken queries for video object segmentation better than direct input of ASR outputs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that audio-conditioned referring video object segmentation benefits from decomposing the process into sequential stages rather than feeding noisy transcripts straight into a segmentation model. Spoken expressions can contain inaccuracies or refer to objects absent from the video, so an early visual existence check allows the system to terminate with empty masks when appropriate. The approach then generates an initial trajectory and refines it through targeted evaluation and possible boundary corrections. This modular structure won first place in the MeViS-Audio track by managing error propagation at each step instead of relying on end-to-end robustness.

Core claim

The central claim is that the MEVIS_Audio task is best solved by a four-stage pipeline: first transcribing long-form spoken input into text, then using an Omni-based module to judge whether the described target is visually present, generating a coarse mask trajectory with Sa2VA if it is, and finally applying an agentic refinement layer that assesses reliability and may invoke SAM3 for improved spatial and temporal precision, with all-zero masks output when the target is absent.

What carries the argument

The four-stage audio-aware Ref-VOS pipeline that converts speech to text, verifies visual existence of the target, produces an initial segmentation trajectory, and performs agent-guided refinement of that trajectory.

Load-bearing premise

That the visual existence verification step can reliably determine whether the transcribed target appears in the video and that the subsequent refinement layer can improve the coarse trajectory without introducing new errors.

What would settle it

A controlled experiment comparing the full staged pipeline against a baseline that sends the same ASR transcripts directly into Sa2VA, measured on videos containing spoken queries that either mismatch the visible content or accurately describe objects not present in the footage.

Figures

Figures reproduced from arXiv: 2604.18665 by Chao Yang, Deshui Miao, Haijun Zhang, Ming-Hsuan Yang, Xin Li, Yameng Gu.

**Figure 1.** Figure 1: Pipeline of our methods. video? To address this question, we employ Qwen3-VL [1] as a visual judge. Given the transcript-derived referring phrase and a set of sampled video frames, the module estimates whether the described entity can be visually grounded in the scene. The result is stored as presence_info.target_exists. This stage serves as an essential robustness mechanism against ASR-induced false po… view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A competition report on a staged audio Ref-VOS pipeline that won first place but supplies no numbers or comparisons to show the added stages actually drove the result.

read the letter

This paper is a report on the system that took first place in the MeViS-Audio track. The authors describe a pipeline that transcribes spoken referring expressions with VibeVoice-ASR, runs an Omni-based check to see whether the target appears in the video, feeds a prompt to Sa2VA for an initial mask trajectory if it does, and then applies an agentic refinement step that can call SAM3 for better boundaries and consistency. They argue this staged setup handles noisy audio inputs better than piping ASR output straight into segmentation.

Referee Report

2 major / 1 minor

Summary. The manuscript describes APRVOS, a staged pipeline for the MEVIS_Audio track of audio-conditioned referring video object segmentation. It converts spoken referring expressions via VibeVoice-ASR, applies an Omni-based module to verify whether the transcribed target is visually present (outputting all-zero masks if absent), feeds the prompt into Sa2VA for a coarse mask trajectory, and then applies an agentic refinement layer that assesses reliability and may invoke SAM3 for improved spatial and temporal precision. The work reports a 1st-place result and argues that the explicit decomposition into transcription, existence verification, coarse segmentation, and refinement is substantially more appropriate than directly passing noisy ASR output to a segmentation model.

Significance. If the reported 1st-place performance is reproducible and the added stages demonstrably improve over direct baselines, the work would illustrate the practical value of modular handling of noisy audio inputs in Ref-VOS, particularly the utility of early termination on absent targets and post-hoc agentic correction. This could serve as a reference architecture for future audio-visual grounding systems. However, the absence of any metrics, ablations, or error breakdowns in the provided description substantially limits the ability to gauge its broader impact or confirm that the stages add net value without introducing new failure modes.

major comments (2)

[Abstract] Abstract: the central claim that the staged design is 'substantially more appropriate' for audio-conditioned Ref-VOS than directly sending noisy ASR outputs into a segmentation model is unsupported by any ablation studies, head-to-head comparisons against a direct ASR-to-Sa2VA baseline, or quantitative error analysis of the Omni judgment and agentic refinement steps. This evidence is load-bearing for the paper's contribution and the reported 1st-place result.
[Abstract] Abstract: no performance metrics, leaderboard scores, ablation tables, or error rates for the visual existence verification module are supplied, preventing assessment of whether the Omni check and subsequent refinement actually drive the winning performance or merely avoid obvious failure cases.

minor comments (1)

[Abstract] The pipeline description would benefit from an explicit diagram or flowchart showing data flow between VibeVoice-ASR, Omni verification, Sa2VA, and the agentic layer.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful review and for highlighting the need for stronger empirical support in our description of the APRVOS pipeline. We address each major comment below and commit to revisions that improve the manuscript's clarity and evidential basis without overstating what the current experiments demonstrate.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the staged design is 'substantially more appropriate' for audio-conditioned Ref-VOS than directly sending noisy ASR outputs into a segmentation model is unsupported by any ablation studies, head-to-head comparisons against a direct ASR-to-Sa2VA baseline, or quantitative error analysis of the Omni judgment and agentic refinement steps. This evidence is load-bearing for the paper's contribution and the reported 1st-place result.

Authors: We agree that the manuscript does not contain ablation studies or direct head-to-head comparisons against a baseline that omits the existence verification and agentic refinement stages. The 1st-place leaderboard result provides the main empirical indication of overall effectiveness, but it does not isolate the contribution of each stage. In the revised manuscript we will add a new subsection that (a) articulates the design rationale grounded in observed failure modes of direct ASR-to-segmentation pipelines during development, (b) includes any reconstructible quantitative indicators from our competition logs (e.g., frequency of early termination), and (c) tempers the wording of the central claim to reflect the practical advantages observed rather than asserting strict superiority without controlled comparisons. revision: yes
Referee: [Abstract] Abstract: no performance metrics, leaderboard scores, ablation tables, or error rates for the visual existence verification module are supplied, preventing assessment of whether the Omni check and subsequent refinement actually drive the winning performance or merely avoid obvious failure cases.

Authors: The submitted manuscript indeed omits specific numerical results, leaderboard scores, and module-level metrics in order to remain concise. This limits the reader's ability to evaluate the individual stages. We will revise the paper to report the exact leaderboard score achieved, the proportion of cases in which the Omni-based verification triggered early termination, and qualitative examples illustrating the effect of the refinement layer. Where full per-module error rates cannot be recovered from our competition submissions, we will explicitly note the limitation and provide the best available supporting statistics. revision: yes

Circularity Check

0 steps flagged

No circularity detected; modular pipeline uses independent external components without derivations or self-referential reductions

full rationale

The paper describes an audio-conditioned Ref-VOS pipeline that chains VibeVoice-ASR transcription, an Omni-based visual existence verification module, Sa2VA for coarse mask trajectories, and an agentic refinement layer invoking SAM3. No equations, fitted parameters, uniqueness theorems, or ansatzes appear in the provided text. The assertion that the staged design is substantially more appropriate than direct noisy-ASR input is presented as a design rationale rather than a derived result. All steps rely on external, non-self-cited modules, so the description remains self-contained with no reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied systems paper describing a pipeline composed of existing models. No new parameters are fitted, no new axioms are introduced, and no new entities are postulated.

pith-pipeline@v0.9.0 · 5586 in / 1092 out tokens · 80211 ms · 2026-05-10T03:25:09.744663+00:00 · methodology

discussion (0)

Reference graph

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