Contrastive Conditional Latent Diffusion for Audio-visual Segmentation

• URL: http://arxiv.org/abs/2307.16579v2
• Date: Tue, 01 Jul 2025 05:44:57 GMT
• Title: Contrastive Conditional Latent Diffusion for Audio-visual Segmentation
• Authors: Yuxin Mao, Jing Zhang, Mochu Xiang, Yunqiu Lv, Dong Li, Yiran Zhong, Yuchao Dai,
• Abstract summary: We propose a conditional latent diffusion model for audio-visual segmentation (AVS)<n>Our framework incorporates a latent diffusion model to achieve semantic-correlated representation learning.<n>We effectively enhance the contribution of audio for AVS by adopting this latent diffusion model via contrastive learning.
• Score: 42.94080672928958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a contrastive conditional latent diffusion model for audio-visual segmentation (AVS) to thoroughly investigate the impact of audio, where the correlation between audio and the final segmentation map is modeled to guarantee the strong correlation between them. To achieve semantic-correlated representation learning, our framework incorporates a latent diffusion model. The diffusion model learns the conditional generation process of the ground-truth segmentation map, resulting in ground-truth aware inference during the denoising process at the test stage. As our model is conditional, it is vital to ensure that the conditional variable contributes to the model output. We thus extensively model the contribution of the audio signal by minimizing the density ratio between the conditional probability of the multimodal data, e.g. conditioned on the audio-visual data, and that of the unimodal data, e.g. conditioned on the audio data only. In this way, our latent diffusion model via density ratio optimization explicitly maximizes the contribution of audio for AVS, which can then be achieved with contrastive learning as a constraint, where the diffusion part serves as the main objective to achieve maximum likelihood estimation, and the density ratio optimization part imposes the constraint. By adopting this latent diffusion model via contrastive learning, we effectively enhance the contribution of audio for AVS. The effectiveness of our solution is validated through experimental results on the benchmark dataset. Code and results are online via our project page: https://github.com/OpenNLPLab/DiffusionAVS.

Related papers

• Do Audio-Visual Segmentation Models Truly Segment Sounding Objects? [38.98706069359109]
  We introduce AVSBench-Robust, a benchmark incorporating diverse negative audio scenarios including silence, ambient noise, and off-screen sounds. Our approach achieves remarkable improvements in both standard metrics and robustness measures, maintaining near-perfect false positive rates.
  arXiv  Detail & Related papers  (2025-02-01T07:40:29Z)
• D3RM: A Discrete Denoising Diffusion Refinement Model for Piano Transcription [7.108713005834857]
  We present a novel architecture for piano transcription using discrete diffusion models.<n>Our approach outperforms previous diffusion-based piano transcription models and the baseline model in terms of F1 score.
  arXiv  Detail & Related papers  (2025-01-09T08:44:06Z)
• Diffusion-based Unsupervised Audio-visual Speech Enhancement [26.937216751657697]
  This paper proposes a new unsupervised audiovisual speech enhancement (AVSE) approach. It combines a diffusion-based audio-visual speech generative model with a non-negative matrix factorization (NMF) noise model. Experimental results confirm that the proposed AVSE approach not only outperforms its audio-only counterpart but also generalizes better than a recent supervisedgenerative AVSE method.
  arXiv  Detail & Related papers  (2024-10-04T12:22:54Z)
• Mutual Learning for Acoustic Matching and Dereverberation via Visual Scene-driven Diffusion [93.32354378820648]
  We introduce MVSD, a mutual learning framework based on diffusion models. MVSD considers the two tasks symmetrically, exploiting the reciprocal relationship to facilitate learning from inverse tasks. Our framework can improve the performance of the reverberator and dereverberator.
  arXiv  Detail & Related papers  (2024-07-15T00:47:56Z)
• CMMD: Contrastive Multi-Modal Diffusion for Video-Audio Conditional Modeling [21.380988939240844]
  We introduce a multi-modal diffusion model tailored for the bi-directional conditional generation of video and audio. We propose a joint contrastive training loss to improve the synchronization between visual and auditory occurrences.
  arXiv  Detail & Related papers  (2023-12-08T23:55:19Z)
• DiffSED: Sound Event Detection with Denoising Diffusion [70.18051526555512]
  We reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process. During training, our model learns to reverse the noising process by converting noisy latent queries to the groundtruth versions.
  arXiv  Detail & Related papers  (2023-08-14T17:29:41Z)
• Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS [0.0]
  The diffusion model is capable of generating high-quality data through a probabilistic approach. It suffers from the drawback of slow generation speed due to the requirement of a large number of time steps. We propose a speech synthesis model with two discriminators: a diffusion discriminator for learning the distribution of the reverse process and a spectrogram discriminator for learning the distribution of the generated data.
  arXiv  Detail & Related papers  (2023-08-03T07:22:04Z)
• An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization [58.88327181933151]
  In this paper, we propose an efficient query-based membership inference attack (MIA) Experimental results indicate that the proposed method can achieve competitive performance with only two queries on both discrete-time and continuous-time diffusion models. To the best of our knowledge, this work is the first to study the robustness of diffusion models to MIA in the text-to-speech task.
  arXiv  Detail & Related papers  (2023-05-26T16:38:48Z)
• VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation [88.49030739715701]
  This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation.
  arXiv  Detail & Related papers  (2023-03-15T02:16:39Z)
• Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval [7.459223771397159]
  Cross-modal data (e.g. audiovisual) have different distributions and representations that cannot be directly compared. To bridge the gap between audiovisual modalities, we learn a common subspace for them by utilizing the intrinsic correlation in the natural synchronization of audio-visual data with the aid of annotated labels. We propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss.
  arXiv  Detail & Related papers  (2022-11-07T10:37:14Z)
• Audio-visual speech enhancement with a deep Kalman filter generative model [0.0]
  We present an audiovisual deep Kalman filter (AV-DKF) generative model which assumes a first-order Markov chain model for the latent variables. We develop an efficient inference methodology to estimate speech signals at test time.
  arXiv  Detail & Related papers  (2022-11-02T09:50:08Z)
• TransFusion: Transcribing Speech with Multinomial Diffusion [20.165433724198937]
  We propose a new way to perform speech recognition using a diffusion model conditioned on pretrained speech features. We demonstrate comparable performance to existing high-performing contrastive models on the LibriSpeech speech recognition benchmark. We also propose new techniques for effectively sampling and decoding multinomial diffusion models.
  arXiv  Detail & Related papers  (2022-10-14T10:01:43Z)
• How Much is Enough? A Study on Diffusion Times in Score-based Generative Models [76.76860707897413]
  Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution. We show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process.
  arXiv  Detail & Related papers  (2022-06-10T15:09:46Z)
• Self-Supervised Learning for speech recognition with Intermediate layer supervision [52.93758711230248]
  We propose Intermediate Layer Supervision for Self-Supervised Learning (ILS-SSL) ILS-SSL forces the model to concentrate on content information as much as possible by adding an additional SSL loss on the intermediate layers. Experiments on LibriSpeech test-other set show that our method outperforms HuBERT significantly.
  arXiv  Detail & Related papers  (2021-12-16T10:45:05Z)
• Diffusion-Based Representation Learning [65.55681678004038]
  We augment the denoising score matching framework to enable representation learning without any supervised signal. In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective. Using the same approach, we propose to learn an infinite-dimensional latent code that achieves improvements of state-of-the-art models on semi-supervised image classification.
  arXiv  Detail & Related papers  (2021-05-29T09:26:02Z)
• Deep Variational Generative Models for Audio-visual Speech Separation [33.227204390773316]
  We propose an unsupervised technique based on audio-visual generative modeling of clean speech. To better utilize the visual information, the posteriors of the latent variables are inferred from mixed speech. Our experiments show that the proposed unsupervised VAE-based method yields better separation performance than NMF-based approaches.
  arXiv  Detail & Related papers  (2020-08-17T10:12:33Z)
• Denoising Diffusion Probabilistic Models [91.94962645056896]
  We present high quality image synthesis results using diffusion probabilistic models. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics.
  arXiv  Detail & Related papers  (2020-06-19T17:24:44Z)

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.