Jianchao Zhao — accepted AI-conference papers

2026

Is Parameter Isolation Better for Prompt-Based Continual Learning?

Jiangyang Li, Chenhao Ding, SongLin Dong, Qiang Wang, Jianchao Zhao, Yuhang He +1

CVPR 2026

Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilization. To address this, we consider the practical n

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2026

Shared & Domain Self-Adaptive Experts with Frequency-Aware Discrimination for Continual Test-Time Adaptation

Jianchao Zhao, Chenhao Ding, SongLin Dong, Jiangyang Li, Qiang Wang, Yuhang He +1

AAAI 2026technical

This paper focuses on the Continual Test-Time Adaptation (CTTA) task, aiming to enable an agent to continuously adapt to evolving target domains while retaining previously acquired domain knowledge for effective reuse when those domains reappear. Existing shared-parameter paradigms struggle to balan

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