Benefiting from the low latency and high dynamic range, event cameras have recently been adopted for Optical Flow (OF) prediction under harsh environments with high-speed motion or extreme lighting conditions. However, the emitted events only ...

• Alleviating the imperfection of events by utilizing the spatiotemporal attention-based module.
• Fully exploiting the cross-modal characteristics between frames and events.
• Designing long-term motion information for global temporal ...

Unsupervised Domain Adaptation (UDA) is an effective technique for utilizing labeled data from a source domain alongside unlabeled data from a target domain, aiming to mitigate the impact of domain shift on model performance. Feature decoupling-...

Unsupervised domain adaptation (UDA) has recently garnered widespread attention in the field of medical image segmentation by transferring knowledge from labeled source datasets to enhance model segmentation performance on unlabeled target domain ...

Automatic segmentation of brain glioma from magnetic resonance imaging (MRI) using deep learning methods is very important for clinical diagnosis and follow-up treatments. The size, shape and location of glioma vary greatly among different ...

In medical image segmentation tasks, the performance of the trained segmentation model in the unseen domain is affected by the domain shifting problem. Therefore, improving the model’s generalization is crucial for the practical application of ...

In this paper, we generalize the log-Sobolev inequalities to Rényi–Sobolev inequalities by replacing the entropy with the two-parameter entropy, which is a generalized version of entropy and closely related to Rényi divergences. We ...

In this paper, we derive variational formulas for the asymptotic exponents (i.e., convergence rates) of the concentration and isoperimetric functions in the product Polish probability space under certain mild assumptions. These formulas are expressed in ...

Multi-Agent Deep Reinforcement Learning (MADRL) is a research field that combines deep learning and multi-agent reinforcement learning. In complex tasks, a single agent often finds it difficult to complete the task independently, thus requiring ...

Multimodal medical image fusion aims to improve the clinical practicability of medical images by integrating complementary information from multiple medical images. However, in traditional fusion methods, the fusion rules based on prior knowledge ...

Accurate and robust localization is one of the most basic functional modules of mobile robots. A recent promising approach is to employ a camera that captures ground textures to achieve high-precision localization. However, low robustness and ...

Most artificial lights exhibit subtle fluctuations in intensity and frequency in response to the influence of the grid's alternating current, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based ...

Recently, object detection based on deep neural network has become the mainstream research direction of sweeping robots since different obstacles may produce completely contradictory sweeping decisions. However, due to the limited computational ...

FANET (Fly-Adhoc-Network) does not rely on pre-built infrastructure, and can form a temporary network through wireless links anytime and anywhere, which has been widely used in emergency communication and disaster relief. In order to solve the problem of ...

In this paper, we study the type graph, namely, a bipartite graph induced by a joint type. We investigate the maximum edge density of induced bipartite subgraphs of this graph having a number of vertices on each side on an exponential scale in the length <...

This study addresses the problem of estimating the parameters of a single chirp signal affected by Wiener phase noise with an unknown variance and observed in an additive white Gaussian noise (AWGN) environment. We derive the time-domain joint maximum ...

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To develop a lossless distributed algorithm for regularized Cox proportional hazards model with variable selection to support federated learning for vertically distributed data.

We propose a novel distributed algorithm for ...

The stereo event-intensity camera setup is widely applied to leverage the advantages of both event cameras with low latency and intensity cameras that capture accurate brightness and texture information. However, such a setup commonly encounters cross-...

Bimodal objects, such as the checkerboard pattern used in camera calibration, markers for object tracking, and text on road signs, to name a few, are prevalent in our daily lives and serve as a visual form to embed information that can be easily ...

Conventional cameras face challenges when capturing motion information during the exposure due to their physical design, rendering the motion deblurring task ill-posed. To this end, we propose a Two-stage Residual-based Motion Deblurring (TRMD) framework ...

Few-shot learning is attracting extensive research because of its ability to classify only a few co-trainable samples. Current few-shot learning approaches focus on learning class prototypes representation to solve problems by a simple averaging ...

• Introduced a novel approach for few-shot image classification.
• Innovatively transformed the process of assessing sample importance into graph label propagation.
• Achieved performance improvements on few-shot benchmark datasets.