30 citations · 33 across the 7 of their papers we have counts for
7 papers
RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation
Chang Nie, Guangming Wang, Zhe Liu +3
Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorith…
HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction
Qijie Bai, Changli Nie, Haiwei Zhang +2
Temporal link prediction, aiming to predict future edges between paired nodes in a dynamic graph, is of vital importance in diverse applications. However, existing methods are main…
: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
Qijie Bai, Jiawen Guo, Haiwei Zhang +3
Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving…
ABC: Attention with Bilinear Correlation for Infrared Small Target Detection
Peiwen Pan, Huan Wang, Chenyi Wang +1
Infrared small target detection (ISTD) has a wide range of applications in early warning, rescue, and guidance. However, CNN based deep learning methods are not effective at segmen…
TaCo: Textual Attribute Recognition via Contrastive Learning
Chang Nie, Yiqing Hu, Yanqiu Qu +3
As textual attributes like font are core design elements of document format and page style, automatic attributes recognition favor comprehensive practical applications. Existing ap…
STN: Scalable Tensorizing Networks via Structure-Aware Training and Adaptive Compression
Chang Nie, Huan Wang, Lu Zhao
Deep neural networks (DNNs) have delivered a remarkable performance in many tasks of computer vision. However, over-parameterized representations of popular architectures dramatica…
Multi-Tensor Network Representation for High-Order Tensor Completion
Chang Nie, Huan Wang, Zhihui Lai
This work studies the problem of high-dimensional data (referred to as tensors) completion from partially observed samplings. We consider that a tensor is a superposition of multip…