9 accepted papers
Foundation Models (FMs) have demonstrated strong generalization across diverse vision tasks. However, their deployment in federated settings is hindered by high computational demands, substantial communication overhead, and significant inference costs. We propose DSFedMed, a dual-scale federated fra
One-shot federated learning (OSFL) reduces the communication cost and privacy risks of iterative federated learning by constructing a global model with a single round of communication. However, most existing methods struggle to achieve robust performance on real-world domains such as medical imaging
Physiological signals such as EEG, ECG, and PPG are widely used in clinical monitoring. Recent self-supervised learning (SSL) methods offer an attractive way to leverage unlabeled recordings, yet they still fall short in practice. In particular, current SSL methods struggle across heterogeneous data
Recently, numerous robust image hashing schemes have been developed for content identification. However, many of these schemes face the challenges of maintaining discrimination while simultaneously resisting large-scale attacks. In this paper, we propose a robust image hashing scheme based on Contra…
Spiking Neural Networks (SNNs), due to their unique spike-based inference mechanism, offer low power consumption and biological plausibility. As a fundamental technology for various real-world applications, 3D point cloud analysis faces significant challenges related to high computational overhead a…
As Large Language Models (LLMs) become more advanced, the security risks they pose also increase. Ensuring that LLM behavior aligns with human values, particularly in mitigating jailbreak attacks with elusive and implicit intentions, has become a significant challenge. To address this issue, we prop…
Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to unlabeled target domains. This addresses the challenge of different distributions across domains, commonly known as domain shift. Numerous methods attempt to align distributions across domains while…
Synthesis-based steganography without embedding (SWE) methods transform secret messages to container images synthesised by generative networks, which eliminates distortions of container images and thus can fundamentally resist typical steganalysis tools. However, existing methods suffer from weak me…
The depth image based rendering (DIBR) plays a key role in 3D video synthesis, by which other virtual views can be generated from a 2D video and its depth map. However, in the synthesis process, the background occluded by the foreground objects might be exposed in the new view, resulting in some hol…