10 citations · 13 across the 6 of their papers we have counts for
6 papers
Building Lightweight Semantic Segmentation Models for Aerial Images Using Dual Relation Distillation
Minglong Li, Lianlei Shan, Weiqiang Wang +3
Recently, there have been significant improvements in the accuracy of CNN models for semantic segmentation. However, these models are often heavy and suffer from low inference spee…
RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
Shuo Yang, Yuqin Dai, Guoqing Wang +6
Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. H…
Transferable and Forecastable User Targeting Foundation Model
Bin Dou, Baokun Wang, Yun Zhu +11
User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital mark…
E-ANT: A Large-Scale Dataset for Efficient Automatic GUI NavigaTion
Ke Wang, Tianyu Xia, Zhangxuan Gu +5
Online GUI navigation on mobile devices has driven a lot of attention recent years since it contributes to many real-world applications. With the rapid development of large languag…
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Yunfei Liu, Jintang Li, Yuehe Chen +9
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning…
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
Xinting Liao, Chaochao Chen, Weiming Liu +7
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective i…