This comprehensive review delves into the pivotal role of prompt engineering in unleashing
the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence …

Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …

The promising zero-shot generalization of vision-language models such as CLIP has led to
their adoption using prompt learning for numerous downstream tasks. Previous works have …

With the emergence of pre-trained vision-language models like CLIP how to adapt them to
various downstream classification tasks has garnered significant attention in recent …

Prompt learning has emerged as a valuable technique in enhancing vision-language
models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly …

Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …

Abstract Pre-trained Vision-Language Models (VLMs) have served as excellent foundation
models for transfer learning in diverse downstream tasks. However tuning VLMs for few-shot …

This work breaks through the Base-New Tradeoff (BNT) dilemma in prompt tuning ie the
better the tuned model generalizes to the base (or target) task the worse it generalizes to …

Prompt tuning represents a valuable technique for adapting pre-trained visual-language
models (VLM) to various downstream tasks. Recent advancements in CoOp-based methods …

CLIP has demonstrated marked progress in visual recognition due to its powerful pre-
training on large-scale image-text pairs. However it still remains a critical challenge: how to …