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Found 4 papers, 1 papers with code
Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning
To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session.
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions.
Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach
In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes.
RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data
In RDIS, we generate extra missing values by applying a random drop on the observed values in incomplete data.
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