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An up-to-date list of works on Multi-Task Learning
| Date | Stars |
|---|---|
| 2026-07-24 | 378 |
| 2026-07-25 | 378 |
| 2026-07-28 | 378 |
| 2026-07-30 | 378 |
| 2026-08-06 | 378 |
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# Awesome Multi-task Learning Feel free to contact me or contribute if you find any interesting paper is missing! ## Table of Contents - [Survey & Study](#survey--study) - [Benchmarks & Code](#benchmarks--code) - [Papers](#papers) - [Awesome Multi-domain Multi-task Learning](#awesome-multi-domain-multi-task-learning) - [Workshops](#workshops) - [Online Courses](#online-courses) - [Related awesome list](#related-awesome-list) ## Survey & Study * Revisit the Imbalance Optimization in Multi-task Learning: An Experimental Analysis (arXiv, 2025) [[paper](https://arxiv.org/abs/2509.23915)] * Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras (arXiv, 2024) [[paper](https://arxiv.org/pdf/2404.18961)] [[code](https://github.com/junfish/Awesome-Multitask-Learning)] * A Survey on Mixture of Experts (arXiv, 2024) [[paper](https://arxiv.org/pdf/2407.06204)] [[code](https://github.com/withinmiaov/A-Survey-on-Mixture-of-Experts)] * Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types (TPAMI, 2022) [[paper](https://arxiv.org/pdf/2103.13318.pdf)] * Multi-Task Learning for Dense Prediction Tasks: A Survey (TPAMI, 2021) [[paper](https://arxiv.org/abs/2004.13379)] [[code](https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch)] * A Survey on Multi-Task Learning (TKDE, 2021) [[paper](https://ieeexplore.ieee.org/abstract/document/9392366)] * Multi-Task Learning with Deep Neural Networks: A Survey (arXiv, 2020) [[paper](http://arxiv.org/abs/2009.09796)] * Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018, **Best Paper**) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zamir_Taskonomy_Disentangling_Task_CVPR_2018_paper.pdf)] [[dataset](http://taskonomy.stanford.edu/)] * A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks (IEEE Access, 2019) [[paper](https://ieeexplore.ieee.org/document/8848395)] * An Overview of Multi-Task Learning in Deep Neural Networks (arXiv, 2017) [[paper](http://arxiv.org/abs/1706.05098)] ## Benchmarks & Code <details> <summary>Benchmarks</summary> ### Dense Prediction Tasks * **[NYUv2]** Indoor Segmentation and Support Inference from RGBD Images (ECCV, 2012) [[paper](https://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf)] [[dataset](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)] * **[Cityscapes]** The Cityscapes Dataset for Semantic Urban Scene Understanding (CVPR, 2016) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780719)] [[dataset](https://www.cityscapes-dataset.com/)] * **[PASCAL-Context]** The Role of Context for Object Detection and Semantic Segmentation in the Wild (CVPR, 2014) [[paper](https://cs.stanford.edu/~roozbeh/pascal-context/mottaghi_et_al_cvpr14.pdf)] [[dataset](https://cs.stanford.edu/~roozbeh/pascal-context/)] * **[Taskonomy]** Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018 [best paper]) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zamir_Taskonomy_Disentangling_Task_CVPR_2018_paper.pdf)] [[dataset](http://taskonomy.stanford.edu/)] * **[KITTI]** Vision meets robotics: The KITTI dataset (IJRR, 2013) [[paper](http://www.cvlibs.net/publications/Geiger2013IJRR.pdf)] [dataset](http://www.cvlibs.net/datasets/kitti/) * **[SUN RGB-D]** SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (CVPR 2015) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298655)] [[dataset](https://rgbd.cs.princeton.edu)] * **[BDD100K]** BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning (CVPR, 2020) [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.pdf)] [[dataset](https://bdd-data.berkeley.edu/)] * **[Omnidata]** Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:63643e9c1bd0bce1, topic:computer-vision, readme:object detection, readme:semantic segmentation
matched fp:63643e9c1bd0bce1, topic:deep-learning