Search Results

The evolution of communication architecture shifts towards virtualization and cloud-native network functions, setting the stage for the flexibility and integration of emerging technologies. Artificial Intelligence (AI) and Machine Learning (ML) as ...

Information extraction is pivotal in natural language processing, where the goal is to convert unstructured text into structured information. A significant challenge in this domain is the diversity and specific needs of various processing tasks. ...

Point cloud segmentation forms the foundation of 3D scene understanding. Boundaries, the intersections of regions, are prone to mis-segmentation. Current point cloud segmentation models exhibit unsatisfactory performance on boundaries. There is limited ...

In this paper, we propose a new Momentum Contrastive Bidirectional Encoding network with S elf-D istillation (MoCoBE-SD) to alleviate the data sparsity and noise issues in sequential recommendation by providing rich informative supervisions from both ...

Fine-grained vision-language retrieval aims to search for corresponding fine-grained images based on a text query, or vice versa. The challenge lies in how to match cross-modal data by learning an effective alignment. This paper proposes a simple ...

Segmenting complex layer structures, including subcutaneous fat, skeletal muscle, and bone in arm musculoskeletal ultrasound (MSKUS), is vital for diagnosing and monitoring the progression of Breast-Cancer-Related Lymphedema (BCRL). Nevertheless, ...

Visual object tracking, which is primarily based on visible light image sequences, encounters numerous challenges in complicated scenarios, such as low light conditions, high dynamic ranges, and background clutter. To address these challenges, ...

Autonomous vehicles rely extensively on perception systems to navigate and interpret their surroundings. Despite significant advancements in these systems recently, challenges persist under conditions like occlusion, extreme lighting, or in ...

Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in practice. This work targets learning 3D-aware GANs from unposed images, ...

Parameter-efficient fine-tuning methods adjust a small subset of parameters in large models, achieving performance comparable to or even surpassing that of models fine-tuned with the full parameter set, and significantly reducing the time and ...

Physical human-robot interaction (pHRI) is widely needed in many fields, such as industrial manipulation, home services, and medical rehabilitation, and puts higher demands on the safety of robots. Due to the uncertainty of the working environment, the ...

Web applications heavily depend on databases, yet the conventional database interface often presents challenges for efficient data utilization. It is imperative to address the considerable demand emanating from a vast array of end users seeking ...$$$$$$$$$$

Multi-task learning (MTL), which aims to make full use of knowledge contained in multiple tasks to enhance overall performance and efficiency, has been broadly applied in recommendations. The main challenge for MTL models is negative transfer. Existing ...

In this paper, we study the budget-constrained profit maximization problem with expensive seed endorsement, a derivation of the well-studied influence maximization and profit maximization in social networks. While existing research requires the ...

While the advent of Deep Reinforcement Learning (DRL) has substantially improved the efficiency of Autonomous Vehicles (AVs), it makes them vulnerable to backdoor attacks that can potentially cause traffic congestion or even collisions. Backdoor ...

Cross-media knowledge transfer endeavors to utilize the knowledge or features obtained from the source modality to enhance performance in the target modality. However, the challenge arises from the modality shift that occurs within heterogeneous ...

God Header Files, like God Classes, pose difficulties in code understanding and lead to slow compilation in code evolution. However, there is little empirical evidence on their prevalence and impact. This study aims to investigate God Header Files in ...

In this paper, we consider distributed quantile regression over a decentralized network, which is an example of a popularly used non-smooth statistical model. Somewhat different from the viewpoint of the optimization literature, we jointly ...

• Establish linear convergence of decentralized estimation for nonsmooth quantile regression.
• Several variants of decentralized gradients methods are compared.
• Numerical demonstrate the performance in comparison with ADMM approach.

The remarkable success of reinforcement learning (RL) heavily relies on observing the reward of every visited state-action pair. In many real world applications, however, an agent can observe only a score that represents the quality of the whole ...