Visual prompt engineering is a fundamental methodology in the field of visual and image
artificial general intelligence. As the development of large vision models progresses, the …

Hallucination posed as a pervasive challenge of multi-modal large language models
(MLLMs) has significantly impeded their real-world usage that demands precise judgment …

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 …

Despite substantial progress all-in-one image restoration (IR) grapples with persistent
challenges in handling intricate real-world degradations. This paper introduces MPerceiver …

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 …

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 …

In computer vision, fine-tuning is the de-facto approach to leverage pre-trained vision
models to perform downstream tasks. However, deploying it in practice is quite challenging …

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 …

Large Language Models (LLMs) are transforming artificial intelligence, enabling
autonomous agents to perform diverse tasks across various domains. These agents …

This paper addresses text-supervised semantic segmentation aiming to learn a model
capable of segmenting arbitrary visual concepts within images by using only image-text …