Dynamic Derivation and Elimination: Audio Visual Segmentation with Enhanced Audio Semantics

• URL: http://arxiv.org/abs/2503.12840v1
• Date: Mon, 17 Mar 2025 05:38:05 GMT
• Title: Dynamic Derivation and Elimination: Audio Visual Segmentation with Enhanced Audio Semantics
• Authors: Chen Liu, Liying Yang, Peike Li, Dadong Wang, Lincheng Li, Xin Yu,
• Abstract summary: We propose Dynamic Derivation and Elimination (DDESeg): a novel audio-visual segmentation framework.<n>To mitigate feature confusion, DDESeg reconstructs the semantic content of the mixed audio signal.<n>To reduce the matching difficulty, we introduce a discriminative feature learning module.
• Score: 26.399212357764576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sound-guided object segmentation has drawn considerable attention for its potential to enhance multimodal perception. Previous methods primarily focus on developing advanced architectures to facilitate effective audio-visual interactions, without fully addressing the inherent challenges posed by audio natures, \emph{\ie}, (1) feature confusion due to the overlapping nature of audio signals, and (2) audio-visual matching difficulty from the varied sounds produced by the same object. To address these challenges, we propose Dynamic Derivation and Elimination (DDESeg): a novel audio-visual segmentation framework. Specifically, to mitigate feature confusion, DDESeg reconstructs the semantic content of the mixed audio signal by enriching the distinct semantic information of each individual source, deriving representations that preserve the unique characteristics of each sound. To reduce the matching difficulty, we introduce a discriminative feature learning module, which enhances the semantic distinctiveness of generated audio representations. Considering that not all derived audio representations directly correspond to visual features (e.g., off-screen sounds), we propose a dynamic elimination module to filter out non-matching elements. This module facilitates targeted interaction between sounding regions and relevant audio semantics. By scoring the interacted features, we identify and filter out irrelevant audio information, ensuring accurate audio-visual alignment. Comprehensive experiments demonstrate that our framework achieves superior performance in AVS datasets.

Related papers

• Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment [26.399212357764576]
  Accurately localizing audible objects based on audio-visual cues is the core objective of audio-visual segmentation.<n>We propose a novel framework with two primary components: an audio-guided modality alignment (AMA) module and an uncertainty estimation (UE) module.<n>AMA performs audio-visual interactions within multiple groups and consolidates group features into compact representations based on their responsiveness to audio cues.<n>UE integrates spatial and temporal information to identify high-uncertainty regions caused by frequent changes in sound state.
  arXiv  Detail & Related papers  (2025-03-17T05:48:22Z)
• Can Textual Semantics Mitigate Sounding Object Segmentation Preference? [10.368382203643739]
  We argue that audio lacks robust semantics compared to vision, resulting in weak audio guidance over the visual space. Motivated by the the fact that text modality is well explored and contains rich abstract semantics, we propose leveraging text cues from the visual scene to enhance audio guidance. Our method exhibits enhanced sensitivity to audio when aided by text cues, achieving highly competitive performance on all three subsets.
  arXiv  Detail & Related papers  (2024-07-15T17:45:20Z)
• Bootstrapping Audio-Visual Segmentation by Strengthening Audio Cues [75.73217916395386]
  We propose a Bidirectional Audio-Visual Decoder (BAVD) with integrated bidirectional bridges. This interaction narrows the modality imbalance, facilitating more effective learning of integrated audio-visual representations. We also present a strategy for audio-visual frame-wise synchrony as fine-grained guidance of BAVD.
  arXiv  Detail & Related papers  (2024-02-04T03:02:35Z)
• Cooperative Dual Attention for Audio-Visual Speech Enhancement with Facial Cues [80.53407593586411]
  We focus on leveraging facial cues beyond the lip region for robust Audio-Visual Speech Enhancement (AVSE) We propose a Dual Attention Cooperative Framework, DualAVSE, to ignore speech-unrelated information, capture speech-related information with facial cues, and dynamically integrate it with the audio signal for AVSE.
  arXiv  Detail & Related papers  (2023-11-24T04:30:31Z)
• QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition [47.103732403296654]
  Multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We introduce a global-to-local quantization mechanism, which distills knowledge from stable global (clip-level) features into local (frame-level) ones. Experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance.
  arXiv  Detail & Related papers  (2023-09-29T20:48:44Z)
• Improving Audio-Visual Segmentation with Bidirectional Generation [40.78395709407226]
  We introduce a bidirectional generation framework for audio-visual segmentation. This framework establishes robust correlations between an object's visual characteristics and its associated sound. We also introduce an implicit volumetric motion estimation module to handle temporal dynamics.
  arXiv  Detail & Related papers  (2023-08-16T11:20:23Z)
• Transavs: End-To-End Audio-Visual Segmentation With Transformer [33.56539999875508]
  We propose TransAVS, the first Transformer-based end-to-end framework for Audio-Visual task. TransAVS disentangles the audio stream as audio queries, which will interact with images and decode into segmentation masks. Our experiments demonstrate that TransAVS achieves state-of-the-art results on the AVSBench dataset.
  arXiv  Detail & Related papers  (2023-05-12T03:31:04Z)
• Language-Guided Audio-Visual Source Separation via Trimodal Consistency [64.0580750128049]
  A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform. We adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets.
  arXiv  Detail & Related papers  (2023-03-28T22:45:40Z)
• Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention [54.4258176885084]
  How to accurately recognize ambiguous sounds is a major challenge for audio captioning. We propose visually-aware audio captioning, which makes use of visual information to help the description of ambiguous sounding objects. Our proposed method achieves state-of-the-art results on machine translation metrics.
  arXiv  Detail & Related papers  (2022-10-28T22:45:41Z)
• Visual Sound Localization in the Wild by Cross-Modal Interference Erasing [90.21476231683008]
  In real-world scenarios, audios are usually contaminated by off-screen sound and background noise. We propose the Interference Eraser (IEr) framework, which tackles the problem of audio-visual sound source localization in the wild.
  arXiv  Detail & Related papers  (2022-02-13T21:06:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.