arxiv: 2604.23935 · v1 · submitted 2026-04-27 · 💻 cs.CV

Recognition: unknown

2nd of the 5th PVUW MeViS-Audio Track: ASR-SaSaSa2VA

Zhiyu Wang , Xudong Kang , Shutao Li

Authors on Pith no claims yet

Pith reviewed 2026-05-08 04:47 UTC · model grok-4.3

classification 💻 cs.CV

keywords audio-based video object segmentationautomatic speech recognitionreferring video segmentationno-target detectiontext-based segmentation modelsPVUW challenge

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The pith

Audio cues are converted to text descriptions via ASR for use with pre-trained video segmentation models.

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

The paper aims to show that audio-based video object segmentation can be handled efficiently without training large end-to-end models. It does this by running automatic speech recognition on the audio to produce text descriptions of motion and objects. These texts are passed to existing text-based referring video segmentation models. An added module detects and drops audio clips that do not point to any target object. The resulting system placed second with a score of 80.7 in the MeViS-v2-Audio track of the PVUW challenge.

Core claim

The central claim is that converting audio inputs into textual motion descriptions through automatic speech recognition, then feeding those descriptions into pre-trained text-based referring video segmentation models such as SaSaSa2VA, combined with a no-target expression detection module using a fine-tuned audio MLLM, produces accurate pixel-level object segmentations while remaining resource-efficient and robust to irrelevant audio.

What carries the argument

The ASR-to-text conversion pipeline that turns audio into motion descriptions, which are then processed by a pre-trained text-based referring video segmentation model (e.g., SaSaSa2VA) together with a no-target detection filter.

If this is right

The method avoids the computational cost and data requirements of end-to-end audio-visual fusion models.
The no-target detection module prevents erroneous segmentations when audio does not refer to any object.
Strong performance can be achieved by combining separate pre-trained components rather than training a single joint model.
Temporal alignment between audio and video is handled implicitly through the text descriptions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar transcription-based decoupling could allow text models to serve other audio-conditioned visual tasks without full retraining.
Improvements in ASR accuracy would directly raise segmentation quality without any change to the visual model.
The approach questions whether tightly fused multimodal models are required when a modular text bridge suffices.

Load-bearing premise

Automatic speech recognition will reliably produce accurate enough motion descriptions that the downstream text-based segmentation model can use to locate the correct objects.

What would settle it

A set of test videos in which the ASR output contains clear errors in describing the motion or target, yet the audio itself unambiguously refers to a visible object, leading to segmentation failures.

Figures

Figures reproduced from arXiv: 2604.23935 by Shutao Li, Xudong Kang, Zhiyu Wang.

**Figure 1.** Figure 1: Overview of our method. ASR-SaSaSa2VA decomposes the task into three stages: (1) converting audio inputs into textual motion descriptions via automatic speech recognition models, (2) performing text-based referring video segmentation using a pre-trained model, and (3) filtering out invalid queries through a no-target expression detection module. work, we build upon SaSaSa2VA [7], which extends the Sa2VA fr… view at source ↗

read the original abstract

Audio-based video object segmentation aims to locate and segment objects in videos conditioned on audio cues, requiring precise understanding of both appearance and motion. Recent audio-driven video segmentation methods extend MLLMs by fusing audio and visual features for end-to-end localization. Despite their promise, these approaches are computationally intensive, struggle with aligning temporal audio cues to dynamic video content, and depend on large paired audio-video datasets. To address these challenges, we present ASR-SaSaSa2VA, a resource-efficient framework for audio-guided video segmentation. The key idea is to convert audio inputs into textual motion descriptions via automatic speech recognition (ASR) models and then leverage pre-trained text-based referring video segmentation models (e.g., SaSaSa2VA) for pixel-level predictions. To further enhance robustness, we incorporate a no-target expression detection module, implemented by a fine-tuned audio-based MLLM, which filters out audio clips that do not refer to any target object. This design allows the system to exploit strong pre-trained models while effectively handling ambiguous or irrelevant audio inputs. Our approach achieves a final score of 80.7 in the 5th PVUW Challenge (MeViS-v2-Audio track), earning the second-place ranking.

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

This is a clean competition report that gets second place by piping ASR text into a pre-trained referring video segmenter plus a simple no-target filter.

read the letter

The main takeaway is that a straightforward assembly of off-the-shelf ASR and the existing SaSaSa2VA model, with an added audio-based no-target detector, was enough to hit 80.7 and second place on the MeViS-v2-Audio track. Nothing in the approach invents new principles or derives new math; it just shows that converting audio to motion text and reusing a strong text-based segmenter works well enough on this benchmark while staying lighter than end-to-end audio-visual training.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces ASR-SaSaSa2VA, a framework for audio-based video object segmentation. Audio is transcribed to motion descriptions using ASR, which are then fed to a pre-trained text-based referring segmentation model (SaSaSa2VA) to generate pixel-level masks. A no-target detection module based on a fine-tuned audio MLLM filters out audio that does not refer to any object. The system achieves a score of 80.7 on the MeViS-v2-Audio track of the 5th PVUW Challenge, ranking second.

Significance. If the reported performance holds, the work illustrates a practical, resource-efficient alternative to end-to-end audio-visual models by composing existing pre-trained components. This modular design avoids the need for large paired audio-video datasets and heavy computation, which could be valuable for deploying audio-guided segmentation in real-world applications. The competitive ranking provides empirical validation within the challenge setting.

minor comments (3)

[Abstract] The description of the no-target expression detection module is brief; specifying the base MLLM used for fine-tuning and the dataset for fine-tuning would aid reproducibility.
[The framework description] No implementation details such as the specific ASR model (e.g., Whisper variant) or inference settings are provided, which limits the ability to replicate the 80.7 score.
[Results section] The paper would benefit from a brief comparison to other participating methods or to a simple baseline to contextualize the 80.7 score.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, recognition of the practical value of our modular approach, and recommendation for minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No significant circularity in empirical competition report

full rationale

The paper is a short descriptive systems report of a competition entry with no mathematical derivation chain, equations, fitted parameters, or generalization claims. It describes an ASR-to-text pipeline feeding a pre-trained referring segmentation model plus a no-target filter, then reports the empirical outcome (80.7 score, second place) on the MeViS-v2-Audio track. No step reduces by construction to its own inputs, no self-citation is load-bearing for any premise, and the result is externally falsifiable via the public challenge evaluation. This is a standard honest non-finding for an applied engineering report.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an engineering systems paper that assembles pre-trained models; no new free parameters, axioms, or invented entities are introduced or fitted.

pith-pipeline@v0.9.0 · 5527 in / 1074 out tokens · 69774 ms · 2026-05-08T04:47:33.662257+00:00 · methodology

discussion (0)

Reference graph

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