11 citations · 17 across the 7 of their papers we have counts for
7 papers
Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation
Linhan Wang, Shuo Lei, Jianfeng He +3
Remote sensing image semantic segmentation is an important problem for remote sensing image interpretation. Although remarkable progress has been achieved, existing deep neural net…
Med-MMHL: A Multi-Modal Dataset for Detecting Human- and LLM-Generated Misinformation in the Medical Domain
Yanshen Sun, Jianfeng He, Shuo Lei +2
The pervasive influence of misinformation has far-reaching and detrimental effects on both individuals and society. The COVID-19 pandemic has witnessed an alarming surge in the dis…
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
Shuo Lei, Xuchao Zhang, Jianfeng He +2
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches h…
Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training
Jianfeng He, Julian Salazar, Kaisheng Yao +2
End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of collecting speech-semantics pairs, especially when label domains change. Hence, we explore \texti…
LANS: Large-scale Arabic News Summarization Corpus
Abdulaziz Alhamadani, Xuchao Zhang, Jianfeng He +1
Text summarization has been intensively studied in many languages, and some languages have reached advanced stages. Yet, Arabic Text Summarization (ATS) is still in its developing…
Semantic Editing On Segmentation Map Via Multi-Expansion Loss
Jianfeng He, Xuchao Zhang, Shuo Lei +4
Semantic editing on segmentation map has been proposed as an intermediate interface for image generation, because it provides flexible and strong assistance in various image genera…
Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations
Shuo Lei, Xuchao Zhang, Jianfeng He +2
Despite the great progress made by deep neural networks in the semantic segmentation task, traditional neural-networkbased methods typically suffer from a shortage of large amounts…