Aligned Contrastive Loss for Long-Tailed Recognition
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912408948178944 |
|---|---|
| author | Ma, Jiali Cui, Jiequan Kazuki, Maeno Subramanian, Lakshmi Jayashree, Karlekar Pranata, Sugiri Zhang, Hanwang |
| author_facet | Ma, Jiali Cui, Jiequan Kazuki, Maeno Subramanian, Lakshmi Jayashree, Karlekar Pranata, Sugiri Zhang, Hanwang |
| contents | In this paper, we propose an Aligned Contrastive Learning (ACL) algorithm to address the long-tailed recognition problem. Our findings indicate that while multi-view training boosts the performance, contrastive learning does not consistently enhance model generalization as the number of views increases. Through theoretical gradient analysis of supervised contrastive learning (SCL), we identify gradient conflicts, and imbalanced attraction and repulsion gradients between positive and negative pairs as the underlying issues. Our ACL algorithm is designed to eliminate these problems and demonstrates strong performance across multiple benchmarks. We validate the effectiveness of ACL through experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist datasets. Results show that ACL achieves new state-of-the-art performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01071 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aligned Contrastive Loss for Long-Tailed Recognition Ma, Jiali Cui, Jiequan Kazuki, Maeno Subramanian, Lakshmi Jayashree, Karlekar Pranata, Sugiri Zhang, Hanwang Computer Vision and Pattern Recognition In this paper, we propose an Aligned Contrastive Learning (ACL) algorithm to address the long-tailed recognition problem. Our findings indicate that while multi-view training boosts the performance, contrastive learning does not consistently enhance model generalization as the number of views increases. Through theoretical gradient analysis of supervised contrastive learning (SCL), we identify gradient conflicts, and imbalanced attraction and repulsion gradients between positive and negative pairs as the underlying issues. Our ACL algorithm is designed to eliminate these problems and demonstrates strong performance across multiple benchmarks. We validate the effectiveness of ACL through experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist datasets. Results show that ACL achieves new state-of-the-art performance. |
| title | Aligned Contrastive Loss for Long-Tailed Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.01071 |