LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised
  Learning in Open-World Scenarios
Long-tailed learning has garnered increasing attention due to its wide
applicability in real-world scenarios. Among existing approaches, Long-Tailed
Semi-Supervised Learning (LTSSL) has emerged as an effective solution by
incorporating a large amount of unlabeled data into the imbalanced labeled
dataset. However, most prior LTSSL methods are designed to train models from
scratch, which often leads to issues such as overconfidence and low-quality
pseudo-labels. To address these challenges, we extend LTSSL into the foundation
model fine-tuning paradigm and propose a novel framework: LoFT (Long-tailed
semi-supervised learning via parameter-efficient Fine-Tuning). We demonstrate
that fine-tuned foundation models can generate more reliable pseudolabels,
thereby benefiting imbalanced learning. Furthermore, we explore a more
practical setting by investigating semi-supervised learning under open-world
conditions, where the unlabeled data may include out-of-distribution (OOD)
samples. To handle this problem, we propose LoFT-OW (LoFT under Open-World
scenarios) to improve the discriminative ability. Experimental results on
multiple benchmarks demonstrate that our method achieves superior performance
compared to previous approaches, even when utilizing only 1\% of the unlabeled
data compared with previous works.
68c77c65ee0eed1697d6b70b
Jiahao Chen
68c77c65ee0eed1697d6b70c
Zhiyuan Huang
claimed_verified
2025-09-15T15:07:39.529Z
67ee66655b1015962a7747db
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/B-bvsSkQ59cWPmaPhFmg5.png
黄致远
user
gamesliker
68c77c65ee0eed1697d6b70d
Yurou Liu
admin_assigned
2025-09-15T15:23:31.281Z
67ac2166ac8a6496920601c7
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/j0lvhfUwA2Xw1if0SuNJP.png
Yurou Liu
user
lyr1ssr
68c77c65ee0eed1697d6b70e
Bing Su
LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised
  Learning in Open-World Scenarios
Long-tailed learning has garnered increasing attention due to its wide
applicability in real-world scenarios. Among existing approaches, Long-Tailed
Semi-Supervised Learning (LTSSL) has emerged as an effective solution by
incorporating a large amount of unlabeled data into the imbalanced labeled
dataset. However, most prior LTSSL methods are designed to train models from
scratch, which often leads to issues such as overconfidence and low-quality
pseudo-labels. To address these challenges, we extend LTSSL into the foundation
model fine-tuning paradigm and propose a novel framework: LoFT (Long-tailed
semi-supervised learning via parameter-efficient Fine-Tuning). We demonstrate
that fine-tuned foundation models can generate more reliable pseudolabels,
thereby benefiting imbalanced learning. Furthermore, we explore a more
practical setting by investigating semi-supervised learning under open-world
conditions, where the unlabeled data may include out-of-distribution (OOD)
samples. To handle this problem, we propose LoFT-OW (LoFT under Open-World
scenarios) to improve the discriminative ability. Experimental results on
multiple benchmarks demonstrate that our method achieves superior performance
compared to previous approaches, even when utilizing only 1\% of the unlabeled
data compared with previous works.
LoFT, a parameter-efficient fine-tuning framework for long-tailed semi-supervised learning, improves reliability of pseudolabels and discriminative ability in open-world scenarios, outperforming previous methods.
Long-Tailed Semi-Supervised Learning LTSSL parameter-efficient fine-tuning pseudolabels open-world scenarios out-of-distribution discriminative ability