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20162024
most citedERNIE: Enhanced Representation through Knowledge Integration

773 citations · 1.1k across the 14 of their papers we have counts for

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6 papers · 1 filter

cs.CV20231 cited

StoryBench: A Multifaceted Benchmark for Continuous Story Visualization

Emanuele Bugliarello, Hernan Moraldo, Ruben Villegas +7

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst be…

cs.CV2022

Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment

Hang Zhang, Su Yang, Hongyong Wang +2

Few researches have studied simultaneous detection of smoke and flame accompanying fires due to their different physical natures that lead to uncertain fluid patterns. In this stud…

cs.CV2018

A Unified Mammogram Analysis Method via Hybrid Deep Supervision

Rongzhao Zhang, Han Zhang, Albert C. S. Chung

Automatic mammogram classification and mass segmentation play a critical role in a computer-aided mammogram screening system. In this work, we present a unified mammogram analysis…

cs.CV2018

Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis

Weizheng Yan, Han Zhang, Jing Sui +1

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitiv…

cs.CV2017158 cited

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Tao Xu, Pengchuan Zhang, Qiuyuan Huang +4

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. Wi…

cs.CV2017

Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision

Mohamed Elhoseiny, Yizhe Zhu, Han Zhang +1

In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to c…