# Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation

arXiv: https://arxiv.org/abs/2104.02026 · HTML: https://mlsanity.com/paper/2104.02026/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 53 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Audio-Visual Scene Analysis with Self-Supervised Multisensory Features](https://mlsanity.com/paper/1804.03641/) (2018)
- [The Sound of Pixels](https://mlsanity.com/paper/1804.03160/) (2018)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss](https://mlsanity.com/paper/1905.03820/) (2019)
- [EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition](https://mlsanity.com/paper/1908.08498/) (2019)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Learning to Localize Sound Source in Visual Scenes](https://mlsanity.com/paper/1803.03849/) (2018)
- [Self-Supervised Learning of Audio-Visual Objects from Video](https://mlsanity.com/paper/2008.04237/) (2020)
- [Listen to Look: Action Recognition by Previewing Audio](https://mlsanity.com/paper/1912.04487/) (2019)
- [Lip Movements Generation at a Glance](https://mlsanity.com/paper/1803.10404/) (2018)
- [The Sound of Motions](https://mlsanity.com/paper/1904.05979/) (2019)
- [Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2007.10558/) (2020)
- [SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition](https://mlsanity.com/paper/1904.04289/) (2019)
- [Multiple Sound Sources Localization from Coarse to Fine](https://mlsanity.com/paper/2007.06355/) (2020)
- [Learning to Separate Object Sounds by Watching Unlabeled Video](https://mlsanity.com/paper/1804.01665/) (2018)
- [Music Gesture for Visual Sound Separation](https://mlsanity.com/paper/2004.09476/) (2020)
- [Co-Separating Sounds of Visual Objects](https://mlsanity.com/paper/1904.07750/) (2019)
- [Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching](https://mlsanity.com/paper/2010.05466/) (2020)
- [Dual-modality seq2seq network for audio-visual event localization](https://mlsanity.com/paper/1902.07473/) (2019)
- [Self-supervised Moving Vehicle Tracking with Stereo Sound](https://mlsanity.com/paper/1910.11760/) (2019)
- [Sep-Stereo: Visually Guided Stereophonic Audio Generation by Associating Source Separation](https://mlsanity.com/paper/2007.09902/) (2020)
- [Recursive Visual Sound Separation Using Minus-Plus Net](https://mlsanity.com/paper/1908.11602/) (2019)

## Built on by (most cited first, 30 of 59)

- [Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation](https://mlsanity.com/paper/2104.11116/) (2021)
- [Learning to Answer Questions in Dynamic Audio-Visual Scenarios](https://mlsanity.com/paper/2203.14072/) (2022)
- [Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation](https://mlsanity.com/paper/2203.13161/) (2022)
- [A Closer Look at Weakly-Supervised Audio-Visual Source Localization](https://mlsanity.com/paper/2209.09634/) (2022)
- [Vision Transformers are Parameter-Efficient Audio-Visual Learners](https://mlsanity.com/paper/2212.07983/) (2022)
- [Mix and Localize: Localizing Sound Sources in Mixtures](https://mlsanity.com/paper/2211.15058/) (2022)
- [Audio-Visual Grouping Network for Sound Localization from Mixtures](https://mlsanity.com/paper/2303.17056/) (2023)
- [Visually Informed Binaural Audio Generation without Binaural Audios](https://mlsanity.com/paper/2104.06162/) (2021)
- [Learning in Audio-visual Context: A Review, Analysis, and New Perspective](https://mlsanity.com/paper/2208.09579/) (2022)
- [Egocentric Audio-Visual Object Localization](https://mlsanity.com/paper/2303.13471/) (2023)
- [Audio-Visual Class-Incremental Learning](https://mlsanity.com/paper/2308.11073/) (2023)
- [Class-aware Sounding Objects Localization via Audiovisual Correspondence](https://mlsanity.com/paper/2112.11749/) (2021)
- [BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge](https://mlsanity.com/paper/2308.10175/) (2023)
- [Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes](https://mlsanity.com/paper/2203.13412/) (2022)
- [Sound Source Localization is All about Cross-Modal Alignment](https://mlsanity.com/paper/2309.10724/) (2023)
- [Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks](https://mlsanity.com/paper/2311.05152/) (2023)
- [iQuery: Instruments as Queries for Audio-Visual Sound Separation](https://mlsanity.com/paper/2212.03814/) (2022)
- [Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering](https://mlsanity.com/paper/2310.06238/) (2023)
- [WorldScribe: Towards Context-Aware Live Visual Descriptions](https://mlsanity.com/paper/2408.06627/) (2024)
- [Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense Interactions through Masked Modeling](https://mlsanity.com/paper/2312.01017/) (2023)
- [CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos](https://mlsanity.com/paper/2212.07065/) (2022)
- [Class-Incremental Grouping Network for Continual Audio-Visual Learning](https://mlsanity.com/paper/2309.05281/) (2023)
- [A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition](https://mlsanity.com/paper/2305.19458/) (2023)
- [Visual Sound Localization in the Wild by Cross-Modal Interference Erasing](https://mlsanity.com/paper/2202.06406/) (2022)
- [Weakly-Supervised Audio-Visual Segmentation](https://mlsanity.com/paper/2311.15080/) (2023)
- [Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser](https://mlsanity.com/paper/2305.17343/) (2023)
- [Modeling and Driving Human Body Soundfields through Acoustic Primitives](https://mlsanity.com/paper/2407.13083/) (2024)
- [Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation](https://mlsanity.com/paper/2210.16472/) (2022)
- [Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2307.02041/) (2023)
- [High-Quality Visually-Guided Sound Separation from Diverse Categories](https://mlsanity.com/paper/2308.00122/) (2023)

## Abstract (arXiv)

There are rich synchronized audio and visual events in our daily life. Inside the events, audio scenes are associated with the corresponding visual objects; meanwhile, sounding objects can indicate and help to separate their individual sounds in the audio track. Based on this observation, in this paper, we propose a cyclic co-learning (CCoL) paradigm that can jointly learn sounding object visual grounding and audio-visual sound separation in a unified framework. Concretely, we can leverage grounded object-sound relations to improve the results of sound separation. Meanwhile, benefiting from discriminative information from separated sounds, we improve training example sampling for sounding object grounding, which builds a co-learning cycle for the two tasks and makes them mutually beneficial. Extensive experiments show that the proposed framework outperforms the compared recent approaches on both tasks, and they can benefit from each other with our cyclic co-learning.