Audio Visual Segmentation Through Text Embeddings

• URL: http://arxiv.org/abs/2502.16359v2
• Date: Thu, 29 May 2025 07:31:02 GMT
• Title: Audio Visual Segmentation Through Text Embeddings
• Authors: Kyungbok Lee, You Zhang, Zhiyao Duan,
• Abstract summary: Research on Audio-Visual (AVS) suffers from data scarcity due to the high cost of fine-grained manual annotations.<n>Recent works attempt to overcome the challenge of limited data by leveraging the vision foundation model, Segment Anything Model (SAM)<n>We propose textbfAV2T-SAM, a novel framework that bridges audio features with the text embedding space of pre-trained text-prompted SAM.
• Score: 17.285669984798975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of Audio-Visual Segmentation (AVS) is to localize and segment the sounding source objects from video frames. Research on AVS suffers from data scarcity due to the high cost of fine-grained manual annotations. Recent works attempt to overcome the challenge of limited data by leveraging the vision foundation model, Segment Anything Model (SAM), prompting it with audio to enhance its ability to segment sounding source objects. While this approach alleviates the model's burden on understanding visual modality by utilizing knowledge of pre-trained SAM, it does not address the fundamental challenge of learning audio-visual correspondence with limited data. To address this limitation, we propose \textbf{AV2T-SAM}, a novel framework that bridges audio features with the text embedding space of pre-trained text-prompted SAM. Our method leverages multimodal correspondence learned from rich text-image paired datasets to enhance audio-visual alignment. Furthermore, we introduce a novel feature, $\mathbf{\textit{\textbf{f}}_{CLIP} \odot \textit{\textbf{f}}_{CLAP}}$, which emphasizes shared semantics of audio and visual modalities while filtering irrelevant noise. Our approach outperforms existing methods on the AVSBench dataset by effectively utilizing pre-trained segmentation models and cross-modal semantic alignment. The source code is released at https://github.com/bok-bok/AV2T-SAM.

Related papers

• From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data [55.2480439325792]
  Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs.<n>These models are typically adapted from text-based large language models (LLMs) through additional training on audio-related tasks.<n>We propose a data generation framework that produces contrastive-like training data, designed to enhance ALLMs' ability to differentiate between present and absent sounds.
  arXiv  Detail & Related papers  (2025-05-26T16:08:41Z)
• OpenAVS: Training-Free Open-Vocabulary Audio Visual Segmentation with Foundational Models [28.56745509698125]
  We propose OpenAVS, a training-free language-based approach to align audio and visual modalities using text as a proxy for open-vocabulary Audio-Visual (AVS)<n>OpenAVS infers masks through 1) audio-to-text prompt generation, 2) LLM-guided prompt translation, and 3) text-to-visual sounding object segmentation.<n>It surpasses existing unsupervised, zero-shot, and few-shot AVS methods by a significant margin, achieving absolute performance gains of approximately 9.4% and 10.9% in mIoU and F-score, respectively.
  arXiv  Detail & Related papers  (2025-04-30T01:52:10Z)
• Towards Open-Vocabulary Audio-Visual Event Localization [59.23161248808759]
  We introduce the Open-Vocabulary Audio-Visual Event localization problem. This problem requires localizing audio-visual events and predicting explicit categories for both seen and unseen data at inference. We propose the OV-AVEBench dataset, comprising 24,800 videos across 67 real-life audio-visual scenes.
  arXiv  Detail & Related papers  (2024-11-18T04:35:20Z)
• 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)
• Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation [17.123212921673176]
  We propose a Spatio-Temporal, Bi-Visual Attention (ST-B) module integrated into the middle of SAM's encoder and mask decoder. It adaptively updates the audio-visual features to convey the temporal correspondence between the video frames and audio streams. Our proposed model outperforms the state-of-the-art methods on AVS benchmarks, especially with an 8.3% mIoU gain on a challenging multi-sources subset.
  arXiv  Detail & Related papers  (2024-06-10T10:53:23Z)
• Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language [77.33458847943528]
  We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visually aligned features solely through watching videos. We show that DenseAV can discover the meaning'' of words and the location'' of sounds without explicit localization supervision.
  arXiv  Detail & Related papers  (2024-06-09T03:38:21Z)
• Auto-ACD: A Large-scale Dataset for Audio-Language Representation Learning [50.28566759231076]
  We propose an innovative, automatic approach to establish an audio dataset with high-quality captions. Specifically, we construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.5M audio-text pairs. We employ LLM to paraphrase a congruent caption for each audio, guided by the extracted multi-modality clues.
  arXiv  Detail & Related papers  (2023-09-20T17:59:32Z)
• Leveraging Foundation models for Unsupervised Audio-Visual Segmentation [49.94366155560371]
  Audio-Visual (AVS) aims to precisely outline audible objects in a visual scene at the pixel level. Existing AVS methods require fine-grained annotations of audio-mask pairs in supervised learning fashion. We introduce unsupervised audio-visual segmentation with no need for task-specific data annotations and model training.
  arXiv  Detail & Related papers  (2023-09-13T05:05:47Z)
• Text-to-feature diffusion for audio-visual few-shot learning [59.45164042078649]
  Few-shot learning from video data is a challenging and underexplored, yet much cheaper, setup. We introduce a unified audio-visual few-shot video classification benchmark on three datasets. We show that AV-DIFF obtains state-of-the-art performance on our proposed benchmark for audio-visual few-shot learning.
  arXiv  Detail & Related papers  (2023-09-07T17:30:36Z)
• Annotation-free Audio-Visual Segmentation [46.42570058385209]
  We propose a novel pipeline for generating artificial data for the Audio-Visual task without extra manual annotations. We leverage existing image segmentation and audio datasets and match the image-mask pairs with its corresponding audio samples using category labels. We also introduce a lightweight model SAMA-AVS which adapts the pre-trained segment anything model(SAM) to the AVS task.
  arXiv  Detail & Related papers  (2023-05-18T14:52:45Z)
• Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation [18.001730255429347]
  Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. We propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. Experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy.
  arXiv  Detail & Related papers  (2023-04-06T09:54:06Z)
• Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text [69.55642178336953]
  We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection.
  arXiv  Detail & Related papers  (2020-03-27T07:37:15Z)

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.