8 citations · 21 across the 18 of their papers we have counts for
7 papers · 1 filter
Polar-Doc: One-Stage Document Dewarping with Multi-Scope Constraints under Polar Representation
Weiguang Zhang, Qiufeng Wang, Kaizhu Huang
Document dewarping, aiming to eliminate geometric deformation in photographed documents to benefit text recognition, has made great progress in recent years but is still far from b…
Unraveling Batch Normalization for Realistic Test-Time Adaptation
Zixian Su, Jingwei Guo, Kai Yao +3
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to…
Context Does Matter: End-to-end Panoptic Narrative Grounding with Deformable Attention Refined Matching Network
Yiming Lin, Xiao-Bo Jin, Qiufeng Wang +1
Panoramic Narrative Grounding (PNG) is an emerging visual grounding task that aims to segment visual objects in images based on dense narrative captions. The current state-of-the-a…
ShanshuiDaDA: An Interactive, Generative System towards Chinese Shanshui Painting
Aven Le Zhou, Qiufeng Wang, Cheng-Hung Lo +1
Shanshui, which means mountain and water, is an East Asian traditional brush painting involving natural landscapes. This paper proposes an interactive and generative system based o…
A Symbolic Character-Aware Model for Solving Geometry Problems
Maizhen Ning, Qiu-Feng Wang, Kaizhu Huang +1
AI has made significant progress in solving math problems, but geometry problems remain challenging due to their reliance on both text and diagrams. In the text description, symbol…
Learning by Analogy: Diverse Questions Generation in Math Word Problem
Zihao Zhou, Maizhen Ning, Qiufeng Wang +4
Solving math word problem (MWP) with AI techniques has recently made great progress with the success of deep neural networks (DNN), but it is far from being solved. We argue that t…
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection
Jianan Ye, Yijie Hu, Xi Yang +3
Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during trainin…