Abstract 3D human pose estimation has been researched for decades with promising fruits.
3D human pose lifting is one of the promising research directions toward the task where …

Diffusion models are a new class of generative models, and have dramatically promoted
image generation with unprecedented quality and diversity. Existing diffusion models mainly …

Driven by the progress of large-scale pre-training, parameter-efficient transfer learning has
gained immense popularity across different subfields of Artificial Intelligence. The core is to …

Change detection (CD) is a critical task to observe and analyze dynamic processes of land
cover. Although numerous deep-learning (DL)-based CD models have performed …

Multi-label image recognition in the low-label regime is a task of great challenge and
practical significance. Previous works have focused on learning the alignment between …

Segment anything model (SAM), an eminent universal image segmentation model, has
recently gathered considerable attention within the domain of medical image segmentation …

Despite recent promising performances across a range of vision tasks, vision Transformers
still have an issue of high computational costs. Recently, vision prompt learning has …

Detecting abnormal human behaviors in surveillance videos is crucial for various domains,
including security and public safety. Many successful detection techniques based on deep …

Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success
on vision transformers (ViTs) adaptation by improving parameter efficiency. However, the …

The" pre-training and fine-tuning" paradigm in addressing long-tailed recognition tasks has
sparked significant interest since the emergence of large vision-language models like the …