9 accepted papers
The miniaturization of thermal sensors for mobile platforms inherently limits their spatial resolution and textural fidelity, leading to blurry and less informative images. Existing thermal super-resolution (SR) methods can be grouped into single-image and RGB-guided approaches: the former struggles
Deep multi-view clustering draws plentiful attention in various domains, owing to remarkable performance in learning patterns from complementary information of multi-view data. However, previous methods encounter two challenges. They utilize a single pre-defined clustering strategy to perceive diver
Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video efficiency and scalability. While the recent trajectory-based tokenizers offer a promising solution by decoupling video duration from token count, they re
The increasing reliance on multi-sensor systems to enhance the robustness and accuracy of robotic systems introduces new challenges, particularly in calibrating multiple sensors. Traditional offline calibration techniques, which use targets like checkerboards, assume that extrinsic parameters betwee…
Metalenses offer significant potential for ultra-compact computational imaging but face challenges from complex optical degradation and computational restoration difficulties. Existing methods typically rely on precise optical calibration or massive paired datasets, which are non-trivial for real-wo…
Accurate continuous blood glucose prediction is an effective and direct method for treating type 2 diabetes mellitus. However, current methods are commonly single-domain single-scale blood glucose prediction models. That is, they only learn time correlations within constant time steps of continuous…
Multi-view clustering aims to divide samples into several clusters, by mining and utilizing the consistency and complementarity of multi-view data. Recent years, numerous deep contrastive multi-view clustering methods have been proposed to address the false negative issue by using self-supervised in…
To position agents equipped with ultrawideband (UWB) devices without requiring continuous clock synchronization, the primary measurement currently used is the time of flight between agents, estimated through active two-way ranging (TWR). However, the cumbersome signal exchange mechanism in active TW…
Split Federated Learning (SFL) enables scalable training on edge devices by combining the parallelism of Federated Learning (FL) with the computational offloading of Split Learning (SL). Despite its great success, SFL suffers significantly from the well-known straggler issue in distributed learning…