3 accepted papers
The learning order of semantic classes significantly impacts unsupervised domain adaptation for semantic segmentation, especially under adverse weather conditions. Most existing curricula rely on handcrafted heuristics (e.g., fixed uncertainty metrics) and follow a static schedule, which fails to ad
Prompt-based learning improves the performance of Pre-trained Language Models (PLMs) over few-shot learning and is suitable for low-resourced scenarios. However, it is challenging to deploy large PLMs online. Knowledge Distillation (KD) can compress large PLMs into small ones; yet, few-shot KD for p…
Prompt-based learning has improved the few-shot learning performance of large-scale Pre-trained Language Models (PLMs). Yet, it is challenging to deploy large-scale PLMs in resource-constrained environments for online applications. Knowledge Distillation (KD) is a promising approach for PLM compress…