CPS-Prompt delivers 1.6x gains in peak memory, training time, and energy on edge hardware for continual learning while staying within 2% accuracy of top prompt-based baselines.
Learning to prompt for continual learning
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
SinglePrompt achieves state-of-the-art results in task-free online continual learning by replacing prompt selection with a single prompt per attention block, cosine-based classifier logits, and masking unexposed classes.
HSA-DINO improves open-vocabulary object detection on domain-shifted tasks via hierarchical semantic prompts and dynamic routing while preserving pre-trained generalization.
citing papers explorer
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Critical Patch-Aware Sparse Prompting with Decoupled Training for Continual Learning on the Edge
CPS-Prompt delivers 1.6x gains in peak memory, training time, and energy on edge hardware for continual learning while staying within 2% accuracy of top prompt-based baselines.
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Is Prompt Selection Necessary for Task-Free Online Continual Learning?
SinglePrompt achieves state-of-the-art results in task-free online continual learning by replacing prompt selection with a single prompt per attention block, cosine-based classifier logits, and masking unexposed classes.
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Parameter-Efficient Semantic Augmentation for Enhancing Open-Vocabulary Object Detection
HSA-DINO improves open-vocabulary object detection on domain-shifted tasks via hierarchical semantic prompts and dynamic routing while preserving pre-trained generalization.