Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …

The visual classification performance of vision-language models such as CLIP has been
shown to benefit from additional semantic knowledge from large language models (LLMs) …

We revisit and advance visual prompting (VP), an input prompting technique for vision tasks.
VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in the …

In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Unlike the unified pre-training strategy employed in the language field, the graph field …

Abstract Multi-modal (vision-language) models, such as CLIP, are replacing traditional
supervised pre-training models (eg, ImageNet-based pre-training) as the new generation of …

Large-scale pre-trained vision models (PVMs) have shown great potential for adaptability
across various downstream vision tasks. However, with state-of-the-art PVMs growing to …

Given the power of vision transformers a new learning paradigm pre-training and then
prompting makes it more efficient and effective to address downstream visual recognition …

Task-conditional architecture offers advantage in parameter efficiency but falls short in
performance compared to state-of-the-art multi-decoder methods. How to trade off …

Abstract Although Multimodal Large Language Models (MLLMs) have demonstrated
promising versatile capabilities their performance is still inferior to specialized models on …