1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models
Yihe Wang, Zhiqiao Kang, Bohan Chen +2
Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events, generally associated with the…
Online In-Context Distillation for Low-Resource Vision Language Models
Zhiqi Kang, Rahaf Aljundi, Vaggelis Dorovatas +1
As the field continues its push for ever more resources, this work turns the spotlight on a critical question: how can vision-language models (VLMs) be adapted to thrive in low-res…
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…
Advancing Prompt-Based Methods for Replay-Independent General Continual Learning
Zhiqi Kang, Liyuan Wang, Xingxing Zhang +1
General continual learning (GCL) is a broad concept to describe real-world continual learning (CL) problems, which are often characterized by online data streams without distinct t…
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
Chenxing Hong, Yan Jin, Zhiqi Kang +4
Existing continual learning literature relies heavily on a strong assumption that tasks arrive with a balanced data stream, which is often unrealistic in real-world applications. I…