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https://github.com/ankitshah009/awesome-weak-supervision

Repository consisting of important research papers on weak supervision - Image, Audio, Video
https://github.com/ankitshah009/awesome-weak-supervision
List: awesome-weak-supervision
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Repository consisting of important research papers on weak supervision - Image, Audio, Video
Host: GitHub
URL: https://github.com/ankitshah009/awesome-weak-supervision
Owner: ankitshah009
License: bsd-3-clause
Created: 2021-05-30T11:43:40.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2021-05-30T15:34:54.000Z (over 3 years ago)
Last Synced: 2024-05-22T07:02:52.834Z (4 months ago)
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Watchers: 3
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

        # awesome-weak-supervision

Repository consisting of important research papers on weak supervision - Image, Audio, Video.

Papers covering multiple sub-areas are listed in both the sections. If there are any areas, papers, and datasets I missed, please let me know or feel free to make a pull request.

It would be greatly appreciated if you find this collection useful to star the repository. 

# Weak Supervision in Images

## Research Papers 

 [Exploring the limits of Weakly Supervised Pretraining](https://arxiv.org/abs/1805.00932)

## GitHub Repositories

 [WSLImages](https://github.com/facebookresearch/WSL-Images)

# Weak Supervision in Video

# Weakly Supervised Sound Event Detection

	

## Table of Contents

* [Survey Papers](#survey-papers)

* [Core Areas](#core-areas)

  * [Learning formulation](#learning-formulation)

  * [Network architecture](#network-architecture)

  * [Pooling fuctions](#pooling-functions)

  * [Missing or noisy audio](#missing-or-noisy-audio)

  * [Generative Learning](#generative-learning)

  * [Representation Learning](#representation-learning)

  * [Multi-Task Learning](#multitask-learning)

  * [Adversarial Attacks](#adversarial-attacks)

  * [Few-Shot Learning](#few-shot-learning)

  * [Knowledge-transfer](#knowledge-transfer)

  * [Polyphonic SED](#polyphonic)

  * [Joint learning](#joint-task)

  * [Loss function](#loss)

* [Extension](#applications-and-datasets)

  * [Multimodal: Audio and Visual](#multimodal-audio-visual)

  * [Multimodal: Audio and Text](#multimodal-audio-text)

  * [Strongly and Weakly labelled data](#strong-weak)

  * [Healthcare](#healthcare)

  * [Robotics](#robotics)

* [Dataset](#dataset)

* [Workshops/Conferences/Journals](#WCJ)

* [Tutorials](#tutorials)

* [Courses](#courses)

# Research papers

## Survey papers

[Sound event detection and time–frequency segmentation from weakly labelled data](https://ieeexplore.ieee.org/abstract/document/8632940), TASLP 2019

## Core Areas

### Learning formulation

[Weakly supervised scalable audio content analysis](https://ieeexplore.ieee.org/abstract/document/7552989), ICME 2016

[Audio Event Detection using Weakly Labeled Data](https://dl.acm.org/doi/abs/10.1145/2964284.2964310), 24th ACM Multimedia Conference 2016

[An approach for self-training audio event detectors using web data](https://ieeexplore.ieee.org/abstract/document/8081532), 25th EUSIPCO 2017

[A joint detection-classification model for audio tagging of weakly labelled data](https://ieeexplore.ieee.org/abstract/document/7952234), ICASSP 2017

[Connectionist Temporal Localization for Sound Event Detection with Sequential Labeling](https://ieeexplore.ieee.org/abstract/document/8682278), ICASSP 2019

[Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection](https://arxiv.org/abs/2008.07085), ArXiv 2020

[A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition](https://arxiv.org/abs/2007.00144), ICML 2020

[Non-Negative Matrix Factorization-Convolutional Neural Network (NMF-CNN) For Sound Event Detection](https://arxiv.org/abs/2001.07874), ArXiv 2020

[Duration robust weakly supervised sound event detection](https://arxiv.org/abs/1904.03841), ICASSP 2020

[SeCoST:: Sequential Co-Supervision for Large Scale Weakly Labeled Audio Event Detection](https://ieeexplore.ieee.org/abstract/document/9053613), ICASSP 2020

[Guided Learning for Weakly-Labeled Semi-Supervised Sound Event Detection](https://ieeexplore.ieee.org/abstract/document/9053584), ICASSP 2020

[Unsupervised Contrastive Learning of Sound Event Representations](https://arxiv.org/pdf/2011.07616.pdf), ICASSP 2021

[Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events](https://arxiv.org/abs/2102.05288), ICASSP 2021

### Network Architecture

[Weakly-supervised audio event detection using event-specific Gaussian filters and fully convolutional networks](https://ieeexplore.ieee.org/abstract/document/7952264), ICASSP 2017

[Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data](https://arxiv.org/abs/1707.02530), NIPS Workshop on Machine Learning for Audio 2017

[Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network](https://ieeexplore.ieee.org/abstract/document/8461975), ICASSP 2018

[Orthogonality-Regularized Masked NMF for Learning on Weakly Labeled Audio Data](https://ieeexplore.ieee.org/abstract/document/8461293), ICASSP 2018

[Sound event detection and time–frequency segmentation from weakly labelled data](https://ieeexplore.ieee.org/abstract/document/8632940), TASLP 2019

[Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes](https://ieeexplore.ieee.org/abstract/document/8683434), ICASSP 2019

[Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization](https://ieeexplore.ieee.org/abstract/document/9165887), TASLP 2020

[DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification](https://arxiv.org/abs/2007.12864), ArXiv 2020

[Effective Perturbation based Semi-Supervised Learning Method for Sound Event Detection](http://www.interspeech2020.org/index.php?m=content&c=index&a=show&catid=270&id=481), INTERSPEECH 2020

[Weakly-Supervised Sound Event Detection with Self-Attention](https://ieeexplore.ieee.org/abstract/document/9053609), ICASSP 2020

[Improving Deep Learning Sound Events Classifiers using Gram Matrix Feature-wise Correlations](https://arxiv.org/abs/2102.11771), ICASSP 2021

[An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://arxiv.org/abs/2010.13092), ICASSP 2021

[AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778), ArXiv 2021

### Pooling functions

[Frequency-dependent auto-poolingfunction for weakly supervised sound event detection](https://asmp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13636-021-00206-7.pdf), EURASIP Journal on Audio, Speech, Music Processing

[Adaptive Pooling Operators for Weakly Labeled Sound Event Detection](https://dl.acm.org/doi/10.1109/TASLP.2018.2858559), TASLP 2018

[Comparing the Max and Noisy-Or Pooling Functions in Multiple Instance Learning for Weakly Supervised Sequence Learning Tasks](https://www.isca-speech.org/archive/Interspeech_2018/pdfs/0990.pdf), Interspeech 2018

[A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling](https://ieeexplore.ieee.org/abstract/document/8682847), ICASSP 2019

[Hierarchical Pooling Structure for Weakly Labeled Sound Event Detection](https://arxiv.org/pdf/1903.11791.pdf), INTERSPEECH 2019

[Weakly labelled audioset tagging with attention neural networks](https://ieeexplore.ieee.org/abstract/document/8777125), TASLP 2019

[Sound event detection and time–frequency segmentation from weakly labelled data](https://ieeexplore.ieee.org/abstract/document/8632940), TASLP 2019

[Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection](https://arxiv.org/abs/2008.07085), ArXiv 2019

### Missing or noisy audio:

[Sound event detection and time–frequency segmentation from weakly labelled data](https://ieeexplore.ieee.org/abstract/document/8632940), TASLP 2019

[Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection](https://arxiv.org/abs/2008.07085), ArXiv 2019

### Generative Learning:

[Acoustic Scene Generation with Conditional Samplernn](https://ieeexplore.ieee.org/abstract/document/8683727), ICASSP 2019 

### Representation Learning

[Contrastive Predictive Coding of Audio with an Adversary](http://www.interspeech2020.org/index.php?m=content&c=index&a=show&catid=270&id=478), INTERSPEECH 2020

[ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection](https://arxiv.org/abs/2010.15306), ICASSP 2021

### Multi-Task Learning

[Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection](https://arxiv.org/abs/2008.07085), ArXiv 2019

[Multi-Task Learning and post processing optimisation for sound event detection](http://dcase.community/documents/challenge2019/technical_reports/DCASE2019_Cances_69.pdf), DCASE 2019

[Label-efficient audio classification through multitask learning and self-supervision](https://arxiv.org/abs/1910.12587), ICLR 2019

### Knowledge Transfer

[Transfer learning of weakly labelled audio](https://ieeexplore.ieee.org/abstract/document/8169984), WASPAA 2017

[Knowledge Transfer from Weakly Labeled Audio Using Convolutional Neural Network for Sound Events and Scenes](https://ieeexplore.ieee.org/abstract/document/8462200), ICASSP 2018

[PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://ieeexplore.ieee.org/abstract/document/9229505), TASLP 2020

### Polyphonic SED

[A first attempt at polyphonic sound event detection using connectionist temporal classification](https://ieeexplore.ieee.org/abstract/document/8682847), ICASSP 2017

[Polyphonic Sound Event Detection with Weak Labeling](http://www.cs.cmu.edu/~yunwang/papers/cmu-thesis.pdf), Thesis 2018

[Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy](https://arxiv.org/abs/1905.00268), DCASE 2019

[Evaluation of Post-Processing Algorithms for Polyphonic Sound Event Detection](https://ieeexplore.ieee.org/abstract/document/8937143), WASPAA 2019

[Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection](https://ieeexplore.ieee.org/document/9076321), TASLP 2020

### Joint task

[A Joint Separation-Classification Model for Sound Event Detection of Weakly Labelled Data](https://ieeexplore.ieee.org/abstract/document/8462448), ICASSP 2018

[A Joint Framework for Audio Tagging and Weakly Supervised Acoustic Event Detection Using DenseNet with Global Average Pooling](http://www.interspeech2020.org/uploadfile/pdf/Mon-2-8-7.pdf), INTERSPEECH 2020

### Loss function

[Impact of Sound Duration and Inactive Frames on Sound Event Detection Performance](https://arxiv.org/abs/2102.01927v1), ICASSP 2021

## Extension

### Multimodal Audio and Visual

[A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging](https://ieeexplore.ieee.org/abstract/document/8462479), ICASSP 2018

[Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data](https://arxiv.org/abs/2006.01595), IJCAI 2020

### Multimodal Audio and Text

[Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Events](https://arxiv.org/abs/2102.11474), ICASSP 2021

### Strongly and Weakly labelled data

[Audio event and scene recognition: A unified approach using strongly and weakly labeled data](https://ieeexplore.ieee.org/abstract/document/7966293), IJCNN 2017

### Others

[Sound Event Detection Using Point-Labeled Data](https://interactiveaudiolab.github.io/assets/papers/waspaa_2019_kim.pdf), WASPAA 2019

## Dataset

[DCASE 2019 Task 4: Sound event detection in domestic environments](http://dcase.community/challenge2019/task-sound-event-detection-in-domestic-environments)

[DCASE 2018 Task 4: Large-scale weakly labeled semi-supervised sound event detection in domestic environments](http://dcase.community/challenge2018/task-large-scale-weakly-labeled-semi-supervised-sound-event-detection)

[FSD50K: an open dataset of human-labeled sound events](https://annotator.freesound.org/fsd/release/FSD50K/)

[AudioSet: A large-scale dataset of manually annotated audio events](https://research.google.com/audioset)

## Workshops/Conferences/Journals

List of old workshops (archived) and on-going workshops/conferences/journals:

[Machine Learning for Audio Signal Processing, NIPS 2017 workshop](https://nips.cc/Conferences/2017/Schedule?showEvent=8790)

[MLSP: Machine Learning for Signal Processing](https://ieeemlsp.cc/)

[WASPAA: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics](https://www.waspaa.com)

[ICASSP: IEEE International Conference on Acoustics Speech and Signal Processing](https://2021.ieeeicassp.org/)

[INTERSPEECH](https://www.interspeech2021.org/)

[IEEE/ACM Transactions on Audio, Speech and Language Processing](https://dl.acm.org/journal/taslp)

[DCASE](http://dcase.community/)

## Tutorials

## Resources

[Computational Analysis of Sound Scenes and Events](https://www.springer.com/us/book/9783319634494)

## Credits 

By [Ankit Shah](https://ankitshah009.github.io/)

Some of the links here were picked from repository by [Soham Deshmukh](https://soham97.github.io/)