gzwq / awesome-long-tailed Goto Github PK

Sorry, but the paper 'Difficulty-Net: Learning to Predict Difficulties for Long-Tailed Recognition' was accepted for WACV 2023.
Arxiv: https://arxiv.org/abs/2209.02960
Github: https://github.com/hitachi-rd-cv/Difficulty_Net
Can you please add this paper?

Hi,

Thanks for maintaining the list of papers on long-tailed learning.

Can you please update the list with the following related papers:

1. Improving GANs for Long-Tailed Data through Group Spectral Regularization : pdf / project page / code (github) [ECCV 2022]
2. Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data pdf / code (coming soon) [NeurIPS 2022]
3. Class Balancing GAN with a Classifier in the Loop (CBGAN) pdf / code (github) [UAI 2021]

We would appreciate it if you could please include them in your list.

Thanks
Harsh

Hi. Can you please add the paper 'Class-Difficulty Based Methods for Long-Tailed Visual Recognition' (https://doi.org/10.48550/arXiv.2207.14499), which was accepted in IJCV this year?

We would like to introduce 2 new papers regarding imbalanced learning.

1. Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition (AAAI 2023)

Arxiv version is available
https://arxiv.org/abs/2211.13471

GitHub is available
https://github.com/wjun0830/MOVE

The imbalance-MiniKinetics200 dataset is available in
http://115.145.172.53:1215/AAAI_2023_MOVE_longtailed_minikinetics/index.html

This paper handles video-imbalanced learning and proposes a new video-imbalanced benchmarking dataset.

2. Tailoring Self-Supervision for Supervised Learning (ECCV 2022)

Arxiv version is available
https://arxiv.org/abs/2207.10023)

ECCV version is available
https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136850342.pdf

GitHub is available
https://github.com/wjun0830/Localizable-Rotation

This paper handles self-supervision to be applied in the supervised domain, including image imbalance learning as the main benchmark.

We'd really appreciate it if you include these papers in your awesome long-tailed learning repository.