Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

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 …

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning
of vision-language models (VLMs) under the low-data regime, where only a few additional …

Recent advancements in large-scale visual-language pre-trained models have led to
significant progress in zero-/few-shot anomaly detection within natural image domains …

Recent progress in the few-shot adaptation of Vision-Language Models (VLMs) has further
pushed their generalization capabilities at the expense of just a few labeled samples within …

Vision Transformer has demonstrated impressive success across various vision tasks.
However, its heavy computation cost, which grows quadratically with respect to the token …

Exploring spatial-temporal dependencies from observed motions is one of the core
challenges of human motion prediction. Previous methods mainly focus on dedicated …

Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained
language-vision models on downstream tasks with a few labeled samples. While significant …

Multiple instance learning (MIL)-based framework has become the mainstream for
processing the whole slide image (WSI) with giga-pixel size and hierarchical image context …

Test-time adaptation with pre-trained vision-language models has attracted increasing
attention for tackling distribution shifts during the test time. Though prior studies have …