240 citations · 486 across the 15 of their papers we have counts for
16 papers
Adaptive pruning-based Newton's method for distributed learning
Shuzhen Chen, Yuan Yuan, Youming Tao +3
Newton's method leverages curvature information to boost performance, and thus outperforms first-order methods for distributed learning problems. However, Newton's method is not pr…
A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing
Yuan Yuan, Kristen M. Altenburger
The reliability of controlled experiments, commonly referred to as "A/B tests," is often compromised by network interference, where the outcomes of individual units are influenced…
Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms
Yongkang Guo, Yuan Yuan, Jinshan Zhang +3
A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomi…
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
Yang Zhan, Zhitong Xiong, Yuan Yuan
In this paper, we introduce the task of visual grounding for remote sensing data (RSVG). RSVG aims to localize the referred objects in remote sensing (RS) images with the guidance…
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
Yangtao Wang, Xi Shen, Yuan Yuan +5
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With…
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition
Zhitong Xiong, Yuan Yuan, Qi Wang
Indoor scene images usually contain scattered objects and various scene layouts, which make RGB-D scene classification a challenging task. Existing methods still have limitations f…
Investigating and Modeling the Dynamics of Long Ties
Ding Lyu, Yuan Yuan, Lin Wang +2
Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing ne…
Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection
Zhinan Cai, Zhiyu Jiang, Yuan Yuan
Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detect…
MT: Multi-Perspective Feature Learning Network for Scene Text Detection
Chuang Yang, Mulin Chen, Yuan Yuan +1
Text detection, the key technology for understanding scene text, has become an attractive research topic. For detecting various scene texts, researchers propose plenty of detectors…
Deep feature selection-and-fusion for RGB-D semantic segmentation
Yuejiao Su, Yuan Yuan, Zhiyu Jiang
Scene depth information can help visual information for more accurate semantic segmentation. However, how to effectively integrate multi-modality information into representative fe…
Learning Independent Instance Maps for Crowd Localization
Junyu Gao, Tao Han, Qi Wang +2
Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse predic…
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Tao Han, Junyu Gao, Yuan Yuan +1
Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learn…
Focus on Semantic Consistency for Cross-domain Crowd Understanding
Tao Han, Junyu Gao, Yuan Yuan +1
For pixel-level crowd understanding, it is time-consuming and laborious in data collection and annotation. Some domain adaptation algorithms try to liberate it by training models w…
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
Junyu Gao, Qi Wang, Yuan Yuan
Recently, crowd counting is a hot topic in crowd analysis. Many CNN-based counting algorithms attain good performance. However, these methods only focus on the local appearance fea…
Efficient Batch Black-box Optimization with Deterministic Regret Bounds
Yueming Lyu, Yuan Yuan, Ivor W. Tsang
In this work, we investigate black-box optimization from the perspective of frequentist kernel methods. We propose a novel batch optimization algorithm, which jointly maximizes the…
Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising
Yuan Yuan, Xiaojing Dong, Chen Dong +3
Predicting keywords performance, such as number of impressions, click-through rate (CTR), conversion rate (CVR), revenue per click (RPC), and cost per click (CPC), is critical for…