9 papers
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
Qiang Wang, Songlin Dong, Shaokun Wang +5
Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domai…
Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
Shuaike Zhang, Shaokun Wang, Haoyu Tang +2
Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behaviors under closed-loop control. In ECL…
Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery
Jizhou Han, Chenhao Ding, Yuhang He +4
Generalized Category Discovery (GCD) seeks to uncover novel categories in unlabeled data while preserving recognition of known categories, yet prevailing visual-only pipelines and…
GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery
Jizhou Han, Chenhao Ding, SongLin Dong +4
Continual Generalized Category Discovery (C-GCD) requires identifying novel classes from unlabeled data while retaining knowledge of known classes over time. Existing methods typic…
IOTA: Corrective Knowledge-Guided Prompt Learning via Black-White Box Framework
Shaokun Wang, Yifan Yu, Yuhang He +2
Recently, adapting pre-trained models to downstream tasks has attracted increasing interest. Previous Parameter-Efficient-Tuning (PET) methods regard the pre-trained model as an op…
StructAlign: Structured Cross-Modal Alignment for Continual Text-to-Video Retrieval
Shaokun Wang, Weili Guan, Jizhou Han +3
Continual Text-to-Video Retrieval (CTVR) is a challenging multimodal continual learning setting, where models must incrementally learn new semantic categories while maintaining acc…
Consistent Supervised-Unsupervised Alignment for Generalized Category Discovery
Jizhou Han, Shaokun Wang, Yuhang He +5
Generalized Category Discovery (GCD) focuses on classifying known categories while simultaneously discovering novel categories from unlabeled data. However, previous GCD methods fa…
Dynamic Integration of Task-Specific Adapters for Class Incremental Learning
Jiashuo Li, Shaokun Wang, Bo Qian +4
Non-exemplar class Incremental Learning (NECIL) enables models to continuously acquire new classes without retraining from scratch and storing old task exemplars, addressing privac…
Continual Novel Class Discovery via Feature Enhancement and Adaptation
Yifan Yu, Shaokun Wang, Yuhang He +2
Continual Novel Class Discovery (CNCD) aims to continually discover novel classes without labels while maintaining the recognition capability for previously learned classes. The ma…