22 accepted papers
Recently, significant breakthroughs have been made in all-in-one image restoration (AiOIR), which can handle multiple restoration tasks with a single model. However, existing methods typically focus on a specific image domain, such as natural scene, medical imaging, or remote sensing. In this work,…
Object insertion has emerged as a promising augmentation paradigm to solve the label scarcity and long-tail distributions in remote sensing. It aims to generate training samples by synthesizing target instances onto real backgrounds. However, existing methods have three critical issues: (i) Semantic…
Recently, deep image deraining models based on paired datasets have made a series of remarkable progress. However, they cannot be well applied in real-world applications due to the difficulty of obtaining real paired datasets and the poor generalization performance. In this paper, we propose a novel…
All-in-one image restoration aims to recover clear images from various degradation types and levels with a unified model. Nonetheless, the significant variations among degradation types present challenges for training a universal model, often resulting in task interference, where the gradient update…
Federated Active Learning (FAL) aims to learn an effective global model, while minimizing label queries. Owing to privacy requirements, it is challenging to design effective active data selection schemes due to the lack of cross-client query information. In this paper, we bridge this important gap b…
Generative Adversarial Networks (GANs) have been widely applied to image super-resolution (SR) to enhance the perceptual quality. However, most existing GAN-based SR methods typically perform coarse-grained discrimination directly on images and ignore the semantic information of images, making it ch…
3D Gaussian Splatting (3DGS) has shown promising results for Novel View Synthesis. However, while it is quite effective when based on high-quality images, its performance declines as image quality degrades, due to lack of resolution, motion blur, noise, compression artifacts, or other factors common…
In this letter, we propose a new method for visual locomotion controller in quadruped robots, aimed at enhancing their capability to traverse challenging terrain. Our approach integrates computer vision techniques with robust locomotion control to improve terrain traversal. To facilitate terrain per
Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, has attracted significant interest from industry and academia. To allow each data owner (FL client) to train a heterogeneous and personalized local model based on its local data distribution, system resources a…
Personalized federated learning (PFL) is designed for scenarios with non-independent and identically distributed (non-IID) client data. Existing model mixup-based methods, one of the main approaches of PFL, can only extract either global or personalized features during training, thereby limiting eff…
Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in li…
Significant progress in image deblurring has been achieved by deep learning methods especially the remarkable performance of supervised models on paired synthetic data. However real-world quality degradation is more complex than synthetic datasets and acquiring paired data in real-world scenarios po…
Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered. Substantial memory storage and expensive re-tr…
With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance. However, it is still very challenging to widely utilize the supervised methods in real cases due to the lack of paired noisy-clean ima…
Deep learning methods have shown remarkable performance in image denoising, particularly when trained on large-scale paired datasets. However, acquiring such paired datasets for real-world scenarios poses a significant challenge. Although unsupervised approaches based on generative adversarial netwo…
Disturbances widely exist in all control systems. Disturbance observers are commonly employed to estimate the disturbances which are often fully compensated in the control signal. However, one rarely recognized fact is that disturbances may be beneficial to the control performances and thus can be e
Motivated by the achievements in model-based methods and the advances in deep networks, we propose a novel enhanced latent space blind model based deep unfolding network, namely ScaoedNet, for complex real image denoising. It is derived by introducing latent space, noise information, and guidance co…
This letter proposes a real-time optimal robust path following control scheme for planar snake robots without sideslip constraints using model predictive control (MPC). One of the features is that a linear double-integrator model rather than the complex dynamic model of snake robots is used for the
Lampreys’ posterior dual dorsal fins can function as a thrust booster. In addition, the fin-fin interaction of these dorsal fins enables utilization of the energy from the wake, making this species one of the most efficient swimmers. Thus, this dorsal fin configuration can serve as a great source of
Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is still an open problem. To solve this problem, we…
This paper designs a guide rail for a wall-climbing robot based on passive suction cups. The designed guide rail can guarantee stable climbing of a wall-climbing robot. Firstly, to design the parameters of the guide rail, properties of the utilized passive suction cup are experimentally studied. The…
This paper presents and discusses the trajectory tracking control design with friction compensation for a three wheeled omnidirectional mobile robot. Firstly, a dynamic model without considering the friction forces is derived. Part of the control effort is used to compensate the friction effects, wh…