mlsanityData to 7 Oct 2026

Learning in Audio-visual Context: A Review, Analysis, and New Perspective

arXiv 2208.09579 ↗First posted 20 Aug 2022cs.CV cs.AIcited by 44

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Abstract (arXiv)

Sight and hearing are two senses that play a vital role in human communication and scene understanding. To mimic human perception ability, audio-visual learning, aimed at developing computational approaches to learn from both audio and visual modalities, has been a flourishing field in recent years. A comprehensive survey that can systematically organize and analyze studies of the audio-visual field is expected. Starting from the analysis of audio-visual cognition foundations, we introduce several key findings that have inspired our computational studies. Then, we systematically review the recent audio-visual learning studies and divide them into three categories: audio-visual boosting, cross-modal perception and audio-visual collaboration. Through our analysis, we discover that, the consistency of audio-visual data across semantic, spatial and temporal support the above studies. To revisit the current development of the audio-visual learning field from a more macro view, we further propose a new perspective on audio-visual scene understanding, then discuss and analyze the feasible future direction of the audio-visual learning area. Overall, this survey reviews and outlooks the current audio-visual learning field from different aspects. We hope it can provide researchers with a better understanding of this area. A website including constantly-updated survey is released: urlhttps://gewu-lab.github.io/audio-visual-learning/.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 155 of its 283 references that are in the map. First posted on arXiv 20 Aug 2022.

Builds on

155 of 283 references.

References · 155

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38 more
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  3. 2018Audio-Visual Scene Analysis with Self-Supervised Multisensory Featurescited by 335
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 16; 2024: 11; 2025: 14; 2026: 3.

44 papers.

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  1. 2023Audio-Visual Segmentation with Semanticscited by 73
  2. 2024MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistancecited by 72
  3. 2023A Comprehensive Review of Data-Driven Co-Speech Gesture Generationcited by 66
  4. 2023V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Modelscited by 65
  5. 2023Enhancing multimodal cooperation via sample-level modality valuationcited by 43
  6. 2023Chat with the Environment: Interactive Multimodal Perception Using Large Language Modelscited by 42
  7. 2024Diagnosing and Re-learning for Balanced Multimodal Learningcited by 40
  8. 2023Audio-Visual Class-Incremental Learningcited by 33
  9. 2024On-the-fly Modulation for Balanced Multimodal Learningcited by 32
  10. 2023Progressive Spatio-temporal Perception for Audio-Visual Question Answeringcited by 29
  11. 2023Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Modelcited by 25
  12. 2024Label-anticipated Event Disentanglement for Audio-Visual Video Parsingcited by 25
32 more
  1. 2023Class-Incremental Grouping Network for Continual Audio-Visual Learningcited by 22
  2. 2025Multimodal Referring Segmentation: A Surveycited by 22
  3. 2024Stepping Stones: A Progressive Training Strategy for Audio-Visual Semantic Segmentationcited by 21
  4. 2024Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-wise Pseudo Labelingcited by 20
  5. 2023Improving Audio-Visual Video Parsing with Pseudo Visual Labelscited by 18
  6. 2024Quantifying and Enhancing Multi-modal Robustness with Modality Preferencecited by 16
  7. 2025Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperationcited by 16
  8. 2025Audio-Guided Dynamic Modality Fusion with Stereo-Aware Attention for Audio-Visual Navigationcited by 13
  9. 2025Advancing Audio-Visual Navigation Through Multi-Agent Collaboration in 3D Environmentscited by 13
  10. 2025Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perceptioncited by 12
  11. 2023CAD -- Contextual Multi-modal Alignment for Dynamic AVQAcited by 10
  12. 2025Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptationcited by 10
  13. 2023CM-PIE: Cross-modal perception for interactive-enhanced audio-visual video parsingcited by 9
  14. 2024SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State Space Modelcited by 6
  15. 2025AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detectioncited by 6
  16. 2023Robust Cross-Modal Knowledge Distillation for Unconstrained Videoscited by 4
  17. 2024Attend-Fusion: Efficient Audio-Visual Fusion for Video Classificationcited by 3
  18. 2024Unveiling Visual Biases in Audio-Visual Localization Benchmarkscited by 3
  19. 2025From Waveforms to Pixels: A Survey on Audio-Visual Segmentationcited by 3
  20. 2023Balanced Audiovisual Dataset for Imbalance Analysiscited by 2
  21. 2025Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectivescited by 2
  22. 2025Towards Robust Optical-SAR Object Detection under Missing Modalities: A Dynamic Quality-Aware Fusion Frameworkcited by 2
  23. 2025SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chaincited by 1
  24. 2025CMDAR: A Chinese Multi-scene Dynamic Audio Reasoning Benchmark with Diverse Challengescited by 1
  25. 2023Revisiting Pre-training in Audio-Visual Learningcited by 0
  26. 2023More than Vanilla Fusion: a Simple, Decoupling-free, Attention Module for Multimodal Fusion Based on Signal Theorycited by 0
  27. 2024Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semanticscited by 0
  28. 2025Audio-Visual Class-Incremental Learning for Fish Feeding intensity Assessment in Aquaculturecited by 0
  29. 2025OS-HGAdapter: Open Semantic Hypergraph Adapter for Large Language Models Assisted Entropy-Enhanced Image-Text Alignmentcited by 0
  30. 2026Crab+: A Scalable and Unified Audio-Visual Scene Understanding Model with Explicit Cooperationcited by 0
  31. 2026AV-Unified: A Unified Framework for Audio-visual Scene Understandingcited by 0
  32. 2026RA-SSU: Towards Fine-Grained Audio-Visual Learning with Region-Aware Sound Source Understandingcited by 0