12 citations · 13 across the 14 of their papers we have counts for
14 papers
Brain-Inspired Hierarchical Modularity for General Continual Learning
Hongwei Yan, Kanglei Zhou, Qi Cheng +7
Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems operating in changing environmen…
CEL: Continual Ego, Exo, and Ego-Exo Learning
Hongwei Yan, Kanglei Zhou, Yuchen Liu +3
Perception for embodied agents is video-based, often multi-view (ego, exo, or both), and inherently continual, with simultaneous task and viewpoint shifts. Yet continual learning (…
MePo++: Unifying Representation Refinement and Reconciliation for General Continual Learning
Guanglong Sun, Kanglei Zhou, Liyuan Wang +6
General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous data, making it a realistic y…
ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models
Guanglong Sun, Shuang Cui, Bo Lei +6
Test-time adaptation (TTA) of vision-language models (VLMs) is essential for their robust deployment in dynamic, real-world environments. However, existing TTA methods often adapt…
FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning
Hongwei Yan, Guanglong Sun, Kanglei Zhou +3
General continual learning (GCL) challenges intelligent systems to learn from single-pass, non-stationary data streams without clear task boundaries. While recent advances in conti…
TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models
Xin Jin, Yichuan Zhong, Yapeng Tian
Current text-conditioned diffusion editors handle single object replacement well but struggle when a new object and a new style must be introduced simultaneously. We present Twin-P…
Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation
Hai Zhang, Siqi Liang, Li Chen +5
Why must vision-language navigation be bound to detailed and verbose language instructions? While such details ease decision-making, they fundamentally contradict the goal for navi…
MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning
Guanglong Sun, Hongwei Yan, Liyuan Wang +5
To cope with uncertain changes of the external world, intelligent systems must continually learn from complex, evolving environments and respond in real time. This ability, collect…
Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection
Guanglong Sun, Siyuan Zhang, Liyuan Wang +3
Safety post-training can improve the harmfulness and policy compliance of Large Language Models (LLMs), but it may also reduce general utility, a phenomenon often described as the…
Domain Generalizable Continual Learning
Hongwei Yan, Guanglong Sun, Zhiqi Kang +2
To adapt effectively to dynamic real-world environments, intelligent systems must continually acquire new skills while generalizing them to diverse, unseen scenarios. Here, we intr…
Bridging Adaptivity and Safety: Learning Agile Collision-Free Locomotion Across Varied Physics
Yichao Zhong, Chong Zhang, Tairan He +1
Real-world legged locomotion systems often need to reconcile agility and safety for different scenarios. Moreover, the underlying dynamics are often unknown and time-variant (e.g.,…
Right Time to Learn:Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation
Guanglong Sun, Hongwei Yan, Liyuan Wang +3
Knowledge distillation (KD) is a powerful strategy for training deep neural networks (DNNs). Although it was originally proposed to train a more compact "student" model from a larg…
Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation
HongWei Yan, Liyuan Wang, Kaisheng Ma +1
To accommodate real-world dynamics, artificial intelligence systems need to cope with sequentially arriving content in an online manner. Beyond regular Continual Learning (CL) atte…
Diffusion Models for Reinforcement Learning: A Survey
Zhengbang Zhu, Hanye Zhao, Haoran He +5
Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement…