35 citations · 53 across the 11 of their papers we have counts for
11 papers · 1 filter
DocTr: Document Transformer for Structured Information Extraction in Documents
Haofu Liao, Aruni RoyChowdhury, Weijian Li +6
We present a new formulation for structured information extraction (SIE) from visually rich documents. It aims to address the limitations of existing IOB tagging or graph-based for…
Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding
Pengfei Zhu, Mengshi Qi, Xia Li +2
Predicting attention regions of interest is an important yet challenging task for self-driving systems. Existing methodologies rely on large-scale labeled traffic datasets that are…
ConTNet: Why not use convolution and transformer at the same time?
Haotian Yan, Zhe Li, Weijian Li +3
Although convolutional networks (ConvNets) have enjoyed great success in computer vision (CV), it suffers from capturing global information crucial to dense prediction tasks such a…
Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
Xiao-Yun Zhou, Bolin Lai, Weijian Li +12
Landmark localization plays an important role in medical image analysis. Learning based methods, including CNN and GCN, have demonstrated the state-of-the-art performance. However,…
Contour Transformer Network for One-shot Segmentation of Anatomical Structures
Yuhang Lu, Kang Zheng, Weijian Li +8
Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gather…
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
Haomin Chen, Yirui Wang, Kang Zheng +8
Visual cues of enforcing bilaterally symmetric anatomies as normal findings are widely used in clinical practice to disambiguate subtle abnormalities from medical images. So far, i…