Kan Zhou — accepted AI-conference papers

2026

Few-Shot Hybrid Incremental Learning:Continually Learning under Data Scarcity and Task Uncertainty

Yan Li, Yuzhu Shi, Kan Zhou, Shu Zhang, Diqi He, Dingwen Zhang +1

CVPR 2026

The increasing complexity of real-world deployment requires intelligent agents to effectively adapt to non-stationary data streams with stochastic increments under data scarcity. We formally define this challenge as the Few-Shot Hybrid Incremental Learning (FSHIL) paradigm, which reveals a critical

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2026

Semi-Supervised Synthetic Data Generation with Fine-Grained Relevance Control for Short Video Search Relevance Modeling

Haoran Li, Zhiming Su, Junyan Yao, Enwei Zhang, Yang Ji, Yan Chen +3

AAAI 2026technical

Synthetic data is widely adopted in embedding models to ensure diversity in training data distributions across dimensions such as difficulty, length, and language. However, existing prompt-based synthesis methods struggle to capture domain-specific data distributions, particularly in data-scarce dom

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