Large scale language models (LLM) have received significant attention and found diverse
applications across various domains, but their development encounters challenges in real …

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

As the parameter size of large language models (LLMs) continues to expand, there is an
urgent need to address the scarcity of high-quality data. In response, existing research has …

Abstract Multi-label Intention Understanding (MIU) for images is a critical yet challenging
domain, primarily due to the ambiguity of intentions leading to a resource-intensive …

Federated learning presents massive potential for privacy-friendly vision task collaboration.
However, the federated visual performance is deeply affected by backdoor attacks, where …

Prevalent federated learning commonly develops under the assumption that the ideal global
class distributions are balanced. In contrast, real-world data typically follows the long-tailed …

Wildlife ReID involves utilizing visual technology to identify specific individuals of wild
animals in different scenarios, holding significant importance for wildlife conservation …

As an intermediate task in computer vision, multiple pedestrian tracking (MPT) aiming at
tracking the pedestrians from a given video, has attracted attention due to its potential …