Continual learning can empower vision-language models to continuously acquire new
knowledge without the need for access to the entire historical dataset. However mitigating …

Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have
received great performance but they neglect the importance of explicitly modeling the global …

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …

In response to the needs of 6G global communications, satellite communication networks
have emerged as a key solution. However, the large-scale development of satellite …

Learning discriminative task-specific features simultaneously for multiple distinct tasks is a
fundamental problem in multi-task learning. Recent state-of-the-art models consider directly …

Reconstructing the viewed images from human brain activity bridges human and computer
vision through the Brain-Computer Interface. The inherent variability in brain function …

The recent rapid progress in (self) supervised learning models is in large part predicted by
empirical scaling laws: a model's performance scales proportionally to its size. Analogous …

Parameter Efficient Tuning has been an prominent approach to adapt the Large Language
Model to downstream tasks. Most previous works considers adding the dense trainable …

Recently there has been an increased interest in the practical problem of learning multiple
dense scene understanding tasks from partially annotated data where each training sample …