# Prompting Segmentation with Sound Is Generalizable Audio-Visual Source Localizer

arXiv: https://arxiv.org/abs/2309.07929 · HTML: https://mlsanity.com/paper/2309.07929/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 38 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [Learning to Localize Sound Source in Visual Scenes](https://mlsanity.com/paper/1803.03849/) (2018)
- [Localizing Visual Sounds the Hard Way](https://mlsanity.com/paper/2104.02691/) (2021)
- [Foundation Models for Decision Making: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2303.04129/) (2023)
- [Audio-Visual Segmentation](https://mlsanity.com/paper/2207.05042/) (2022)
- [Unified Vision and Language Prompt Learning](https://mlsanity.com/paper/2210.07225/) (2022)
- [Localizing Visual Sounds the Easy Way](https://mlsanity.com/paper/2203.09324/) (2022)
- [A Closer Look at Weakly-Supervised Audio-Visual Source Localization](https://mlsanity.com/paper/2209.09634/) (2022)
- [AVSegFormer: Audio-Visual Segmentation with Transformer](https://mlsanity.com/paper/2307.01146/) (2023)
- [Prompt Consistency for Zero-Shot Task Generalization](https://mlsanity.com/paper/2205.00049/) (2022)
- [AV-SAM: Segment Anything Model Meets Audio-Visual Localization and Segmentation](https://mlsanity.com/paper/2305.01836/) (2023)
- [Prompt Vision Transformer for Domain Generalization](https://mlsanity.com/paper/2208.08914/) (2022)
- [Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation](https://mlsanity.com/paper/2307.13236/) (2023)
- [Class-aware Sounding Objects Localization via Audiovisual Correspondence](https://mlsanity.com/paper/2112.11749/) (2021)
- [Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics](https://mlsanity.com/paper/2307.16620/) (2023)
- [MarginNCE: Robust Sound Localization with a Negative Margin](https://mlsanity.com/paper/2211.01966/) (2022)

## Built on by (most cited first, 30 of 37)

- [Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration](https://mlsanity.com/paper/2505.20256/) (2025)
- [Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes](https://mlsanity.com/paper/2407.10957/) (2024)
- [Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation](https://mlsanity.com/paper/2312.06462/) (2023)
- [Stepping Stones: A Progressive Training Strategy for Audio-Visual Semantic Segmentation](https://mlsanity.com/paper/2407.11820/) (2024)
- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation](https://mlsanity.com/paper/2503.13068/) (2025)
- [SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes](https://mlsanity.com/paper/2506.01558/) (2025)
- [Open-Vocabulary Audio-Visual Semantic Segmentation](https://mlsanity.com/paper/2407.21721/) (2024)
- [Boosting Audio Visual Question Answering via Key Semantic-Aware Cues](https://mlsanity.com/paper/2407.20693/) (2024)
- [Can Textual Semantics Mitigate Sounding Object Segmentation Preference?](https://mlsanity.com/paper/2407.10947/) (2024)
- [CPM: Class-conditional Prompting Machine for Audio-visual Segmentation](https://mlsanity.com/paper/2407.05358/) (2024)
- [Towards Omnimodal Expressions and Reasoning in Referring Audio-Visual Segmentation](https://mlsanity.com/paper/2507.22886/) (2025)
- [Dynamic Derivation and Elimination: Audio Visual Segmentation with Enhanced Audio Semantics](https://mlsanity.com/paper/2503.12840/) (2025)
- [Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment](https://mlsanity.com/paper/2503.12847/) (2025)
- [Multi-modal Transfer Learning between Biological Foundation Models](https://mlsanity.com/paper/2406.14150/) (2024)
- [Do Audio-Visual Segmentation Models Truly Segment Sounding Objects?](https://mlsanity.com/paper/2502.00358/) (2025)
- [Revisiting Audio-Visual Segmentation with Vision-Centric Transformer](https://mlsanity.com/paper/2506.23623/) (2025)
- [Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation](https://mlsanity.com/paper/2406.06163/) (2024)
- [Segment Anything for Videos: A Systematic Survey](https://mlsanity.com/paper/2408.08315/) (2024)
- [Implicit Counterfactual Learning for Audio-Visual Segmentation](https://mlsanity.com/paper/2507.20740/) (2025)
- [SAVE: Segment Audio-Visual Easy way using Segment Anything Model](https://mlsanity.com/paper/2407.02004/) (2024)
- [From Waveforms to Pixels: A Survey on Audio-Visual Segmentation](https://mlsanity.com/paper/2508.03724/) (2025)
- [Delayed Bidirectional Alignment via Disentangled Audio Semantics for Audio-Visual Segmentation](https://mlsanity.com/paper/2512.20117/) (2025)
- [AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting](https://mlsanity.com/paper/2506.01015/) (2025)
- [How Do Optical Flow and Textual Prompts Collaborate to Assist in Audio-Visual Semantic Segmentation?](https://mlsanity.com/paper/2601.08133/) (2026)
- [OpenAVS: Training-Free Open-Vocabulary Audio Visual Segmentation with Foundational Models](https://mlsanity.com/paper/2505.01448/) (2025)
- [Towards Agentic AI for Multimodal-Guided Video Object Segmentation](https://mlsanity.com/paper/2508.10572/) (2025)
- [Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation](https://mlsanity.com/paper/2509.18912/) (2025)
- [Ref-SAM3D: Bridging SAM3D with Text for Reference 3D Reconstruction](https://mlsanity.com/paper/2511.19426/) (2025)
- [AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs](https://mlsanity.com/paper/2606.07643/) (2026)

## Abstract (arXiv)

Never having seen an object and heard its sound simultaneously, can the model still accurately localize its visual position from the input audio? In this work, we concentrate on the Audio-Visual Localization and Segmentation tasks but under the demanding zero-shot and few-shot scenarios. To achieve this goal, different from existing approaches that mostly employ the encoder-fusion-decoder paradigm to decode localization information from the fused audio-visual feature, we introduce the encoder-prompt-decoder paradigm, aiming to better fit the data scarcity and varying data distribution dilemmas with the help of abundant knowledge from pre-trained models. Specifically, we first propose to construct Semantic-aware Audio Prompt (SAP) to help the visual foundation model focus on sounding objects, meanwhile, the semantic gap between the visual and audio modalities is also encouraged to shrink. Then, we develop a Correlation Adapter (ColA) to keep minimal training efforts as well as maintain adequate knowledge of the visual foundation model. By equipping with these means, extensive experiments demonstrate that this new paradigm outperforms other fusion-based methods in both the unseen class and cross-dataset settings. We hope that our work can further promote the generalization study of Audio-Visual Localization and Segmentation in practical application scenarios.