Learning Visual Affordance from Audio

• URL: http://arxiv.org/abs/2512.02005v1
• Date: Mon, 01 Dec 2025 18:58:56 GMT
• Title: Learning Visual Affordance from Audio
• Authors: Lidong Lu, Guo Chen, Zhu Wei, Yicheng Liu, Tong Lu,
• Abstract summary: We introduce Audio-Visual Affordance Grounding (AV-AG), a new task that segments object interaction regions from action sounds.<n>To support this task, we construct the first AV-AG dataset, comprising a large collection of action sounds, object images, and pixel-level affordance annotations.<n>We also propose AVAGFormer, a model equipped with a semantic-conditioned cross-modal mixer and a dual-head decoder.
• Score: 29.90423475741895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Audio-Visual Affordance Grounding (AV-AG), a new task that segments object interaction regions from action sounds. Unlike existing approaches that rely on textual instructions or demonstration videos, which often limited by ambiguity or occlusion, audio provides real-time, semantically rich, and visually independent cues for affordance grounding, enabling more intuitive understanding of interaction regions. To support this task, we construct the first AV-AG dataset, comprising a large collection of action sounds, object images, and pixel-level affordance annotations. The dataset also includes an unseen subset to evaluate zero-shot generalization. Furthermore, we propose AVAGFormer, a model equipped with a semantic-conditioned cross-modal mixer and a dual-head decoder that effectively fuses audio and visual signals for mask prediction. Experiments show that AVAGFormer achieves state-of-the-art performance on AV-AG, surpassing baselines from related tasks. Comprehensive analyses highlight the distinctions between AV-AG and AVS, the benefits of end-to-end modeling, and the contribution of each component. Code and dataset have been released on https://jscslld.github.io/AVAGFormer/.

Related papers

• Revisiting Audio-Visual Segmentation with Vision-Centric Transformer [60.83798235788669]
  Audio-Visual (AVS) aims to segment sound-producing objects in video frames based on the associated audio signal.<n>We propose a new Vision-Centric Transformer framework that leverages vision-derived queries to iteratively fetch corresponding audio and visual information.<n>Our framework achieves new state-of-the-art performances on three subsets of the AVSBench dataset.
  arXiv  Detail & Related papers  (2025-06-30T08:40:36Z)
• AV-CrossNet: an Audiovisual Complex Spectral Mapping Network for Speech Separation By Leveraging Narrow- and Cross-Band Modeling [48.23652686272613]
  This paper introduces AV-CrossNet, an glsav system for speech enhancement, target speaker extraction, and multi-talker speaker separation. AV-CrossNet is extended from the CrossNet architecture, which is a recently proposed network that performs complex spectral mapping for speech separation. Evaluation results demonstrate that AV-CrossNet advances the state-of-the-art performance in all audiovisual tasks, even on untrained and mismatched datasets.
  arXiv  Detail & Related papers  (2024-06-17T15:04:15Z)
• 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)
• CLIP-Powered TASS: Target-Aware Single-Stream Network for Audio-Visual Question Answering [6.719652962434731]
  This paper proposes a new CLIP-powered target-aware single-stream (TASS) network for audio-visual question answering (AVQA) It consists of two key components: the target-aware spatial grounding module (TSG+) and the single-stream joint temporal grounding module (JTG)
  arXiv  Detail & Related papers  (2024-05-13T03:25:15Z)
• Unsupervised Audio-Visual Segmentation with Modality Alignment [42.613786372067814]
  Audio-Visual aims to identify, at the pixel level, the object in a visual scene that produces a given sound. Current AVS methods rely on costly fine-grained annotations of mask-audio pairs, making them impractical for scalability. We propose an unsupervised learning method, named Modality Correspondence Alignment (MoCA), which seamlessly integrates off-the-shelf foundation models.
  arXiv  Detail & Related papers  (2024-03-21T07:56:09Z)
• 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)
• Visually-Guided Sound Source Separation with Audio-Visual Predictive Coding [57.08832099075793]
  Visually-guided sound source separation consists of three parts: visual feature extraction, multimodal feature fusion, and sound signal processing. This paper presents audio-visual predictive coding (AVPC) to tackle this task in parameter harmonizing and more effective manner. In addition, we develop a valid self-supervised learning strategy for AVPC via co-predicting two audio-visual representations of the same sound source.
  arXiv  Detail & Related papers  (2023-06-19T03:10:57Z)
• 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)
• AVATAR: Unconstrained Audiovisual Speech Recognition [75.17253531162608]
  We propose a new sequence-to-sequence AudioVisual ASR TrAnsformeR (AVATAR) trained end-to-end from spectrograms and full-frame RGB. We demonstrate the contribution of the visual modality on the How2 AV-ASR benchmark, especially in the presence of simulated noise. We also create a new, real-world test bed for AV-ASR called VisSpeech, which demonstrates the contribution of the visual modality under challenging audio conditions.
  arXiv  Detail & Related papers  (2022-06-15T17:33:19Z)

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.