Výsledky vyhledávání - "Chen, Zihang"

Report

Continuous Dynamic Modeling via Neural ODEs for Popularity Trajectory Prediction

Autor: Yang, Songbo, Zhao, Ziwei, Chen, Zihang, Zhang, Haotian, Xu, Tong, Zhu, Mengxiao

Popularity prediction for information cascades has significant applications across various domains, including opinion monitoring and advertising recommendations. While most existing methods consider this as a discrete problem, popularity actually evo

Externí odkaz: http://arxiv.org/abs/2410.18742

Zobrazit plný text záznamu

Report

Application of Prompt Learning Models in Identifying the Collaborative Problem Solving Skills in an Online Task

Autor: Zhu, Mengxiao, Wang, Xin, Wang, Xiantao, Chen, Zihang, Huang, Wei

Collaborative problem solving (CPS) competence is considered one of the essential 21st-century skills. To facilitate the assessment and learning of CPS competence, researchers have proposed a series of frameworks to conceptualize CPS and explored way

Externí odkaz: http://arxiv.org/abs/2407.12487

Zobrazit plný text záznamu

Report

EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering

Autor: Wang, Junjue, Zheng, Zhuo, Chen, Zihang, Ma, Ailong, Zhong, Yanfei

Earth vision research typically focuses on extracting geospatial object locations and categories but neglects the exploration of relations between objects and comprehensive reasoning. Based on city planning needs, we develop a multi-modal multi-task

Externí odkaz: http://arxiv.org/abs/2312.12222

Zobrazit plný text záznamu

Report

Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework

Autor: Liu, Zhu, Chen, Zihang, Liu, Jinyuan, Ma, Long, Fan, Xin, Liu, Risheng

The detection of small infrared targets against blurred and cluttered backgrounds has remained an enduring challenge. In recent years, learning-based schemes have become the mainstream methodology to establish the mapping directly. However, these met

Externí odkaz: http://arxiv.org/abs/2309.01099

Zobrazit plný text záznamu

Report

Searching a Compact Architecture for Robust Multi-Exposure Image Fusion

Autor: Liu, Zhu, Liu, Jinyuan, Wu, Guanyao, Chen, Zihang, Fan, Xin, Liu, Risheng

In recent years, learning-based methods have achieved significant advancements in multi-exposure image fusion. However, two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. Reliance on aligned ima

Externí odkaz: http://arxiv.org/abs/2305.12236

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání