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Few-Shot Semantic Segmentation (FSS) aims to tackle the challenge of segmenting novel categories with limited annotated data. However, given the diversity among support-query pairs, transferring meta-knowledge to unseen categories poses a ...

• Propose a novel semi-supervised method to address spatial inconsistency.
• Introduce uncertainty-guided pseudo-labeling denoising.
• Design a prototype-level fusion strategy to improve confusion regions prediction.
• Explore the ...

This paper investigates the finite-time quasi-projective synchronization (FTQPS) issue of fractional-order reaction-diffusion neural networks (FORDNNs). To the best of our knowledge, this paper introduces the concept of FTQPS for the first time. ...

The objective of the relation classification task is to extract relations between entities. Recent studies have found that R-BERT (Wu and He, 2019) based on pre-trained BERT (Devlin et al., 2019) acquires extremely good results in the relation ...

• We take into account the semantic difference between subject and object entities.
• We fully fuse the semantic information between entities and the whole sentence.
• These policies we used enjoy high applicability to different NLP ...

The rapid development of generative artificial intelligence has brought significant opportunities for the advancement of digital humanities research. Intelligent processing of ancient texts, as an essential part of digital humanities, is also undergoing a ...

Few-shot learning is crucial in machine learning and computer vision. It enables models to recognize new objects with limited labeled data, addressing the challenge of data scarcity and expanding the application of machine learning to domains with ...

The aim of multi-exposure fusion (MEF) is to generate a high-dynamic-range-like image from images captured by common cameras under different exposure settings. The existing generative adversarial network (GAN)-based MEF methods have achieved fair ...

This paper deals with the lag projective synchronization (LPS) problem for a class of discrete-time fractional-order quaternion-valued neural networks(DTFO QVNNs) systems with time delays. Firstly, a DTFOQVNNs system with time delay is ...

This work focuses on the finite‐time synchronization (F‐TS) for nonidentical delayed neural networks (DNNs) including Caputo fractional partial differential operator and reaction–diffusion terms. A novel F‐TS lemma is proposed by constructing a ...

The Firefly Algorithm (FA) has demonstrated remarkable effectiveness in tackling a range of optimization issues. Nonetheless, the research carried out so far has primarily concentrated on addressing single-objective and typical multi-objective ...${}_{}$

In Chinese archaeological research, analyzing the evolution of motifs in ancient pottery is crucial for studying the spread and growth of cultures across various eras and regions. However, such analyses are often challenging due to the complexities of ...

The fractional competitive neural networks (FCNNs) with multiple restrictions are considered in this article. Specially, the FCNNs with memristors and time-varying delays in complex field (FCMCNNs) are first introduced, as well as the asymptotic ...

Global localization is a challenging task for intelligent robots, as its accuracy directly contributes to the performance of downstream navigation and planning tasks. However, existing literature focus more on the place retrieval and the success rate of ...

Editing real images authentically while also achieving cross-domain editing remains a challenge. Recent studies have focused on converting real images into latent codes and accomplishing image editing by manipulating these codes. However, merely ...

Higher-order structures of networks, namely, small subgraphs of networks (also called network motifs), are widely known to be crucial and essential to the organization of networks. Several works have studied the community detection problem–a ...

In this paper, we consider the distributed local stochastic gradient descent (SGD) algorithm by parallelizing multiple devices in the setting of stochastic convex optimization (SCO). The losses in the majority of the earlier literatures are required to ...

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that ...

The quasi-synchronization (QS) issues for Caputo delayed Cohen–Grossberg neural networks (CGNNs) are discussed in this article. To begin with, a novel lemma is established by constructing suitable fractional differential inequality. ...

In nighttime driving scenes, due to insufficient and uneven lighting, and the scarcity of high-quality datasets, the miss rate of nighttime pedestrian detection (PD) is much higher than that of daytime. Vision-based distance detection (DD) has the ...

The issues of finite time passivity are explored for BAM reaction–diffusion neural networks including discrete delayed and Caputo fractional partial differential operator. With the help of fractional Lyapunov function and existing ...

This article analyzes the quasi-projective synchronization (QPS) issues of delayed Caputo-type BAM neural networks in the complex field. In order to facilitate calculation and realize QPS, the non-decomposition method is adopted. Moreover, a novel ...