Large-scale pre-trained models have achieved remarkable success in various computer
vision tasks. A standard approach to leverage these models is to fine-tune all model …

Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

In this paper we first assess and harness various Vision Foundation Models (VFMs) in the
context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation …

Abstract Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative
for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only …

In the fashion domain, there exists a variety of vision-and-language (V+ L) tasks, including
cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image …

Parameter-efficient transfer learning (PETL) is an emerging research spot aimed at
inexpensively adapting large-scale pre-trained models to downstream tasks. Recent …

In this paper, we efficiently transfer the surpassing representation power of the vision
foundation models, such as ViT and Swin, for video understanding with only a few trainable …

As the model size of pre-trained language models (PLMs) grows rapidly, full fine-tuning
becomes prohibitively expensive for model training and storage. In vision-and-language …

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 …

In this paper we study the problem of generalizable synthetic image detection aiming to
detect forgery images from diverse generative methods eg GANs and diffusion models …