57 citations · 119 across the 19 of their papers we have counts for
19 papers
T2IW: Joint Text to Image & Watermark Generation
An-An Liu, Guokai Zhang, Yuting Su +3
Recent developments in text-conditioned image generative models have revolutionized the production of realistic results. Unfortunately, this has also led to an increase in privacy…
Variational Label-Correlation Enhancement for Congestion Prediction
Biao Liu, Congyu Qiao, Ning Xu +3
The physical design process of large-scale designs is a time-consuming task, often requiring hours to days to complete, with routing being the most critical and complex step. As th…
Towards Effective Visual Representations for Partial-Label Learning
Shiyu Xia, Jiaqi Lv, Ning Xu +2
Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning…
Learning From Biased Soft Labels
Hua Yuan, Ning Xu, Yu Shi +2
Knowledge distillation has been widely adopted in a variety of tasks and has achieved remarkable successes. Since its inception, many researchers have been intrigued by the dark kn…
Unreliable Partial Label Learning with Recursive Separation
Yu Shi, Ning Xu, Hua Yuan +1
Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. Howe…
Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms
Ningli Xu, Rongjun Qin, Shuang Song
Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex obj…
Progressive Purification for Instance-Dependent Partial Label Learning
Ning Xu, Biao Liu, Jiaqi Lv +2
Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is corre…
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Ning Xu, Congyu Qiao, Jiaqi Lv +2
Multi-label learning (MLL) learns from the examples each associated with multiple labels simultaneously, where the high cost of annotating all relevant labels for each training exa…
Decompositional Generation Process for Instance-Dependent Partial Label Learning
Congyu Qiao, Ning Xu, Xin Geng
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true.…
End-to-end video instance segmentation via spatial-temporal graph neural networks
Tao Wang, Ning Xu, Kean Chen +1
Video instance segmentation is a challenging task that extends image instance segmentation to the video domain. Existing methods either rely only on single-frame information for th…
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing
Jing Shi, Ning Xu, Haitian Zheng +3
Recently, large pretrained models (e.g., BERT, StyleGAN, CLIP) have shown great knowledge transfer and generalization capability on various downstream tasks within their domains. I…
Instance-Dependent Partial Label Learning
Ning Xu, Congyu Qiao, Xin Geng +1
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true.…
Learngene: From Open-World to Your Learning Task
Qiufeng Wang, Xin Geng, Shuxia Lin +3
Although deep learning has made significant progress on fixed large-scale datasets, it typically encounters challenges regarding improperly detecting unknown/unseen classes in the…
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Yuxi Li, Ning Xu, Jinlong Peng +2
In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process t…
A Benchmark and Baseline for Language-Driven Image Editing
Jing Shi, Ning Xu, Trung Bui +3
Language-driven image editing can significantly save the laborious image editing work and be friendly to the photography novice. However, most similar work can only deal with a spe…
Compact Learning for Multi-Label Classification
Jiaqi Lv, Tianran Wu, Chenglun Peng +3
Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC enc…
High-Resolution Deep Image Matting
Haichao Yu, Ning Xu, Zilong Huang +2
Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer…
Solar: solution path averaging for fast and accurate variable selection in high-dimensional data
Ning Xu, Timothy C. G. Fisher
We propose a new variable selection algorithm, subsample-ordered least-angle regression (solar), and its coordinate descent generalization, solar-cd. Solar re-constructs lasso path…
Multi-Label Learning with Label Enhancement
Ruifeng Shao, Ning Xu, Xin Geng
The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical ind…