Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation

arXiv:2509.18912 · cs.CV · Submitted 2025-09-23 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation".

Jane: Here's the gist of the paper. The gist The proposed Frequency-Aware Audio-Visual Segmentation (FAVS) framework rethinks AVS by reformulating it as a frequency-domain decomposition and recomposition problem to address inherent frequency-domain contradictions between audio and visual modalities.

Tom: Problem Formulation The paper identifies a critical challenge in AVS where "there are inherent frequency-domain contradictions between audio and video signals, as illustrated in Fig.

Jane: 1" This contradiction arises because high-frequency components in audio contain "significant noise, which can degrade segmentation accuracy if not properly suppressed" Conversely, high-frequency components in video "capture essential structural details, such as object edges, which are critical for precise segmentation" Existing methods fail because they "do not consider the differences in the modality frequency domain" To solve this, the task is formulated as a "frequency decomposition and recomposition problem" where decomposition isolates modality-specific information and recomposition synergistically integrates these bands Frequency-Domain Enhanced Decomposer (FDED) Module The FDED module is designed to "suppress noise in audio while enhancing edge structure information in visuals, preserving important features in both modalities" The process involves several key steps: 1.

Tom: Initial processing of input features from both modalities through convolutional layers incorporating "depthwise separable convolutions DW C and grouped 1×1 convolutions GroupConv".

Paper summary: Tom: So looking at "Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation," the authors are presenting FAVS, which uses FDED and SCMC to handle those frequency domain contradictions by treating AVS as a frequency decomposition and recomposition problem.

Jane: It’s important to remember that they aren't just claiming a win on a benchmark; they are showing how decomposing the task into distinct frequency bands allows them to precisely separate noise from structural details, which is what matters for accurate segmentation.

Lu: The architecture leverages the transformer structure from Cheng et al. two thousand twenty-two and it seems like their main contribution is explicitly linking those decomposition steps with that mixture of experts approach for consistency <ref:2509.18912#pg3>.

Meng: From an engineering standpoint, the implication is that we might be able to build models that are more robust when dealing with messy audio inputs because the decomposition step cleans up the noise before the main segmentation logic sees it. But what about latency? How much extra processing time does this decomposition and recomposition add to latency? That’s a real question for production use.

Tom: It’s about making sure that high frequency noise doesn't overwhelm the visual information, which is what they aimed to solve by using their specialized modules like FDED and SCMC.

Jane: So, for someone just listening to this show, what does this paper really change? It means future AVS models won't just be looking at a combined feature map; they’ll be analyzing the frequency bands separately and then intelligently recomposing them based on what each band contributes most.

Lu: It pushes the idea that explicit frequency-domain analysis is a necessary step, not just an optional enhancement, when dealing with multimodal inputs that have these known modality contradictions <ref:2509.18912#pg2>.

Meng: I'm still thinking about the practical side of deploying this; how much extra processing time does this decomposition and recomposition actually add to running a model at scale? That’s the real question for production use.

Conclusion: Tom: So we’ve been looking at this paper called "Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation." The core thesis is that frequency domain analysis is necessary to resolve the inherent contradictions between audio and visual signals, and the authors use FAVS to do this.

Jane: Yeah, what I mean is, they’re taking audio and visual segmentation and saying we have to stop treating them as one single blob; you have to separate them by their frequency nature first.

Lu: Exactly. The core idea is to break the task down into frequency bands so you can handle the noise in the audio separately from the structural details in the video, which is what they call a frequency decomposition and recomposition problem <ref:2509.18912#pg2>.

Meng: From a deployment angle, I’m still curious—how much extra computational overhead does this decomposition and recomposition actually add to running a model at scale? That’s the real question for production use.

Lalam: I’ve analyzed their implementation, and it’s based on a transformer architecture from two thousand twenty-two but the specific way they use the residual mechanism for frequency bands is what makes it different <ref:2509.18912#pg3>.

Tom: The authors claim they achieved state-of-the-art results on three datasets—S4, MS3, and AVSS. That’s the number we need to look at when we talk about performance gains <ref:2509.18912#pg3>.

Jane: It means they actually managed to get better segmentation boundaries and better recognition of the sound source in really complex scenes compared to what was out there before.

Lu: The ablation studies show that both their decomposition module, FDED, and their cross-modal consistency module, SCMC, contribute something real to the final score <ref:2509.18912#pg3>.

Meng: But I want to see if those gains hold up when we put this on real traffic; those benchmarks are just static images and audio clips.

Lalam: The authors mention that the integration of these two parts is what really boosts performance, and they even showed that increasing the number of experts in their SCMC module helps before it starts dropping off <ref:2509.18912#pg3>.

Tom: It sounds like the title itself—Frequency-Domain Decomposition and Recomposition—really captures how they are fundamentally changing the way we look at these multimodal problems.

Jane: It’s about taking two inputs, separating them by their frequency nature, and then intelligently putting them back together in a way that makes sense for both modalities simultaneously.

Lu: That’s the ambition here; it moves away from just feature fusion toward structured integration based on signal properties <ref:2509.18912#pg3>.

Meng: So what this means for us in production is that if we want robustness against noisy audio, we might need to build a pipeline that does this explicit frequency analysis first.

Tom: If you were deploying this, the next thing you’d want to know is exactly how much memory and compute those two modules are eating up compared to a standard end-to-end model.

Yunzhe Shen, Kai Peng, Leiye liu, Wei Ji, Jingjing Li, §Miao Zhang*, Yongri Piao, §Huchuan Lu

Dalian University of Technology · Yale University · University of Alberta

cs.CV

Submitted: 2025-09-23

Updated: 2026-10-20

Comments: Accepted for publication in IEEE Transactions on Image Processing (TIP), 2026

Code: https://github.com/happylife-pk/FAVS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 92/100

The gist: The gist The proposed Frequency-Aware Audio-Visual Segmentation (FAVS) framework rethinks AVS by reformulating it as a frequency-domain decomposition and recomposition problem to address inherent

Key concepts

Frequency Decomposition and Recomposition: This approach treats the AVS task as breaking down audio and visual signals into different frequency bands. High frequencies are separated to suppress audio noise while preserving visual edges. These decomposed parts are then recombined in a way that ensures both modalities' critical information is effectively integrated for accurate segmentation.
Frequency-Domain Enhanced Decomposer (FDED) Module: This module uses iterative residual processing and 2D FFT to analyze audio and visual features in the frequency domain. It specifically targets high frequencies to suppress noise in audio while simultaneously enhancing structural details like object edges in the visual data, preserving important features from both modalities.
Synergistic Cross-Modal Consistency (SCMC) Module: The SCMC module uses a mixture-of-experts architecture with dynamic routing to enforce consistent representations between audio and visual features across different frequency bands. Experts specialize in capturing relevant information, and a routing mechanism adaptively selects the best experts to ensure strong cross-modal synergy.
Mixture-of-Experts (MoE) Architecture: The MoE architecture is used within SCMC to allow different parts of the model to specialize in capturing features from distinct frequency bands. A dynamic routing mechanism then decides which specific expert is most useful for a given input, ensuring that audio and visual information are consistently aligned during the fusion process.

Terminology

Consistency — 2× in this paper · explained in 3 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Frequency-Aware Audio-Visual Segmentation — 1× in this paper
Frequency-Domain Enhanced Decomposer — 1× in this paper
Synergistic Cross-Modal Consistency — 1× in this paper

Summary

The gist The proposed Frequency-Aware Audio-Visual Segmentation (FAVS) framework rethinks AVS by reformulating it as a frequency-domain decomposition and recomposition problem to address inherent frequency-domain contradictions between audio and visual modalities.

Problem Formulation

The paper identifies a critical challenge in AVS where there are inherent frequency-domain contradictions between audio and video signals, as illustrated in Fig. 1 This contradiction arises because high-frequency components in audio contain significant noise, which can degrade segmentation accuracy if not properly suppressed Conversely, high-frequency components in video capture essential structural details, such as object edges, which are critical for precise segmentation Existing methods fail because they do not consider the differences in the modality frequency domain To solve this, the task is formulated as a frequency decomposition and recomposition problem where decomposition isolates modality-specific information and recomposition synergistically integrates these bands

Frequency-Domain Enhanced Decomposer (FDED) Module

The FDED module is designed to suppress noise in audio while enhancing edge structure information in visuals, preserving important features in both modalities The process involves several key steps:

Initial processing of input features from both modalities through convolutional layers incorporating depthwise separable convolutions DW C and grouped 1×1 convolutions GroupConv.
Application of a two-dimensional Fast Fourier Transform (2D FFT) F to convert processed feature maps into the frequency domain.
Decomposition of the spectrum using a residual mechanism, where F i rem = F i-1 rem − F i band, i = 1, 2..., N.
Extraction of distinct frequency bands corresponding to high, mid and low-frequency band: (Fh = F 1 band, Fm = F 2 band, Fl = F 3 band) and a residual band Fres.
Enhancing the video modality’s high-frequency band using a 3D convolution operation Conv3D to reinforce edge details and applying a channel attention mechanism CA on the audio modality’s high-frequency band to suppress disruptive noise components.
Transforming the enhanced bands back into the spatial domain using the inverse 2D FFT operation F−1.
Reconstructing final feature maps through modality-specific weighted summations w mod Band to ensure effective preservation of critical frequency information.

Synergistic Cross-Modal Consistency (SCMC) Module

The SCMC module is introduced to enforce consistent representations between audio and visual modalities in the frequency domain It employs a mixture-of-experts (MoE) architecture on cross-modal attention, thereby capturing consistent semantic information across modalities Key components include:

**: Each expert learns to specialize in capturing useful features in distinct frequency bands enriched with audio-visual features. 7. To facilitate specialization, the module employs a SpatialTemporal-Channel feature enhancer (ST C) that facilitates specialization by adaptively emphasizing frequency-specific auditory cues and their visually correlated patterns. 8. The features are further correlated by Bidirectional Cross-modal Attention (BCA) to enforce consistent representations of F e v and F e a between visual and audio modalities in the different frequency domains. 9. A dynamic routing mechanism adaptively selects experts based on the entropy of Wv and Wa, guiding the selection to ensure optimal cross-modal synergy strategies. This mechanism uses the entropy Ea/v to calculate ka/v, which determines the top-ka/v scores and their indices in Wa/v for each sample, constructing a dispatch mask. 10. The final features are aggregated using sparse weights W bar a/v to yield fused features F˜v and F˜a, which integrates the semantic information and extracts cross-modal correlations across reconstructed frequency bands. This approach is specifically designed to restore suppressed features across modalities (e.g., visual cues guiding audio expert allocation and vice versa). The framework achieves state-of-the-art performance on three benchmark datasets, including S4, MS3 and AVSS. It also achieved significant results on the AVSS, with 3.9 MJ improvements and significant MF enhancements of 2.9 over the previous one-stage methods. This confirms that both decomposition and recomposition play indispensable roles in enhancing the model’s capability for fine-grained, accurate segmentation. The study concludes by introducing FAVS as a novel framework that addresses inherent frequency-domain contradictions through FDED and SCMC modules. It achieves state-of-the-art performance on three benchmark AVS datasets (S4, MS3 and AVSS). Furthermore, extensive qualitative visualizations verify the effectiveness of the proposed FDED and SCMC modules. The results demonstrate that our model delivers precise segmentation for enhanced boundary delineation while also exhibiting strong semantic understanding that enables effective recognition of the correct sound source in complex scenes. This work hopes to inspire further research into frequency-domain analysis for audio-visual segmentation. The paper presents quantitative comparisons showing that our model significantly outperforms previous state-of-the-art approaches on S4, MS3 and AVSS datasets. The ablation studies confirm the contribution of both modules to performance, showing that the integration of FDED module has a significant improvement, improving MJ by 1.5 in MS3 dataset and the incorporation of SCMC module yields a further 1.9 increase in MJ. The impact of different expert numbers shows that increasing the number of experts initially improves model performance, peaking at 4 experts before declining with further additions. The clear clustering underscores the experts’ ability to capture unique representations, highlighting their complementary contributions to cross-modal consistency. This work is the first to explicitly integrate frequency-domain decomposition and recomposition into the AVS task, effectively addressing inherent frequencydomain contradictions between audio and visual modalities through our proposed FAVS framework. The paper presents a novel Frequency-Aware Audio-Visual Segmentation framework, termed FAVS, which achieves stateof-the-art (SOTA) performance across three benchmark AVS datasets (S4, MS3 and AVSS). The results show that our model performs better on the more challenging datasets (MS3 and AVSS), demonstrating its effectiveness in complex multi-source scenes. The paper's contribution is the explicit integration of frequency-domain decomposition and recomposition into AVS, effectively addressing inherent frequencydomain contradictions between audio and visual modalities through our proposed FAVS framework. The study provides a novel approach to resolving the inherent frequency-domain contradiction between audio and visual modalities. It is important to note that the paper's methodology involves Fourierbased decomposition on both signals (original bands) to four frequent bands (high, middle, low-frequency bands, and the residual band) and a residual iteration strategy in frequency decomposition to prevent information loss. The paper's framework is based on the transformer architecture (Cheng et al. 2022).

Improvements for AI systems

Immediate improvement of audio-visual segmentation accuracy by explicitly addressing inherent frequency-domain contradictions between audio and visual modalities through a reformulation as a frequency-domain decomposition and recomposition problem. This allows the system to precisely isolate modality-specific semantic and structural information across distinct frequency bands during decomposition, specifically enhancing high-frequency bands for video edges while suppressing noise in audio.
Implementation of the Frequency-Domain Enhanced Decomposer (FDED) module to achieve modality-specific feature enhancement, where it performs a residual iteration strategy in frequency decomposition to prevent information loss. This ensures that the semantic and structural information of the two modalities is discriminatively separated and fully exploited in the frequency domain, leading to better preservation of object edges from high-frequency visual components.
Augmentation of cross-modal consistency by deploying the Synergistic Cross-Modal Consistency (SCMC) module, which leverages a mixture-of-experts architecture to reinforce semantic consistency and modality-specific feature preservation through dynamic expert routing. This mechanism enables the system to dynamically integrate experts for ensuring optimal cross-modal synergy strategies, thereby preserving crucial features that may be misleadingly suppressed in the other modality during decomposition.
Utilization of a dynamic routing mechanism based on entropy (Ea/v = − X Nq We a/v log(We a/v + ϵ)) to guide expert selection, which is superior to fixed top-k routing. This allows the system to adaptively select and weight experts, ensuring that features are integrated based on complementary modalities rather than being limited by pre-defined routing strategies.
Enhancement of feature specialization in SCMC by training each expert to specialize in distinct frequency bands enriched with audio-visual features using a SpatialTemporal-Channel (STC) operation. This facilitates specialization by adaptively emphasizing frequency-specific auditory cues and their visually correlated patterns, which results in consistent learning of frequency-specific cues.