16 papers
Parameterized Prompt for Incremental Object Detection
Zijia An, Boyu Diao, Ruiqi Liu +5
Recent studies have demonstrated that incorporating trainable prompts into pretrained models enables effective incremental learning. However, the application of prompts in incremen…
From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning
Ruiqi Liu, Boyu Diao, Hangda Liu +3
Class-Incremental Learning (CIL) requires models to learn new classes without forgetting old ones. A common method is to freeze a pre-trained model and train a new, lightweight ada…
Semantic-Guided Dynamic Sparsification for Pre-Trained Model-based Class-Incremental Learning
Ruiqi Liu, Boyu Diao, Zijia An +4
Class-Incremental Learning (CIL) requires a model to continually learn new classes without forgetting old ones. A common and efficient solution freezes a pre-trained model and empl…
Teacher-Guided Student Self-Knowledge Distillation Using Diffusion Model
Yu Wang, Chuanguang Yang, Zhulin An +6
Existing Knowledge Distillation (KD) methods often align feature information between teacher and student by exploring meaningful feature processing and loss functions. However, due…
Dynamical Adapter Fusion: Constructing A Global Adapter for Pre-Trained Model-based Class-Incremental Learning
Ruiqi Liu, Boyu Diao, Zijia An +3
Class-Incremental Learning (CIL) requires models to continuously acquire new classes without forgetting previously learned ones. A dominant paradigm involves freezing a pre-trained…
Low-redundancy Distillation for Continual Learning
RuiQi Liu, Boyu Diao, Libo Huang +4
Continual learning (CL) aims to learn new tasks without erasing previous knowledge. However, current CL methods primarily emphasize improving accuracy while often neglecting traini…