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cs.CV2024
LTRL: Boosting Long-tail Recognition via Reflective Learning
Qihao Zhao, Yalun Dai, Shen Lin +3
In real-world scenarios, where knowledge distributions exhibit long-tail. Humans manage to master knowledge uniformly across imbalanced distributions, a feat attributed to their di…
cs.CV2024
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content
Qihao Zhao, Yalun Dai, Hao Li +3
Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper, w…
cs.CV2023
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
Qihao Zhao, Chen Jiang, Wei Hu +2
Recently, multi-expert methods have led to significant improvements in long-tail recognition (LTR). We summarize two aspects that need further enhancement to contribute to LTR boos…