3 papers
cs.LG2026
Multi-level Collaborative Distillation Meets Global Workspace Model: A Unified Framework for OCIL
Shibin Su, Guoqiang Liang, De Cheng +2
Online Class-Incremental Learning (OCIL) enables models to learn continuously from non-i.i.d. data streams. Since samples of the data streams can be seen only once, it is more suit…
cs.CV2025
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
Qirui Wu, Shizhou Zhang, De Cheng +4
Catastrophic forgetting is a critical chanllenge for incremental object detection (IOD). Most existing methods treat the detector monolithically, relying on instance replay or know…
cs.CV2024
Visual Prompt Tuning in Null Space for Continual Learning
Yue Lu, Shizhou Zhang, De Cheng +4
Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL), by selecting and updating relevant prompts in the vision-transformer models. On…