Search Results for author: Yuan Yuan

Found 70 papers, 15 papers with code

Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms

no code implementations10 Feb 2023 Yongkang Guo, Yuan Yuan, Jinshan Zhang, Yuqing Kong, Zhihua Zhu, Zheng Cai

A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms.

Experimental Design

Learning to Simulate Daily Activities via Modeling Dynamic Human Needs

1 code implementation9 Feb 2023 Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, Yong Li

To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.

Imitation Learning Scheduling

Z-SSMNet: A Zonal-aware Self-Supervised Mesh Network for Prostate Cancer Detection and Diagnosis in bpMRI

no code implementations12 Dec 2022 Yuan Yuan, Euijoon Ahn, Dagan Feng, Mohamad Khadra, Jinman Kim

However, existing state of the art AI algorithms which are based on deep learning technology are often limited to 2D images that fails to capture inter-slice correlations in 3D volumetric images.

Self-Supervised Learning

Contactless Oxygen Monitoring with Gated Transformer

no code implementations6 Dec 2022 Hao He, Yuan Yuan, Ying-Cong Chen, Peng Cao, Dina Katabi

With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead.

Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects

no code implementations2 Dec 2022 Qi Wang, Juncheng Wang, Junyu Gao, Yuan Yuan, Xuelong Li

The mainstream crowd counting methods regress density map and integrate it to obtain counting results.

Crowd Counting

DeS3: Attention-driven Self and Soft Shadow Removal using ViT Similarity and Color Convergence

no code implementations15 Nov 2022 Yeying Jin, Wenhan Yang, Wei Ye, Yuan Yuan, Robby T. Tan

In this paper, we present DeS3, a method that removes hard, soft and self shadows based on the self-tuned ViT feature similarity and color convergence.

Image Shadow Removal Shadow Removal

Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces

no code implementations2 Sep 2022 Susan Athey, Dean Karlan, Emil Palikot, Yuan Yuan

Online platforms often face challenges being both fair (i. e., non-discriminatory) and efficient (i. e., maximizing revenue).


MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting

no code implementations14 Aug 2022 PengYu Chen, Junyu Gao, Yuan Yuan, Qi Wang

RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RGB-based methods in scenes with low-illumination or similar backgrounds.

Crowd Counting

Unsupervised Learning for Human Sensing Using Radio Signals

no code implementations6 Jul 2022 Tianhong Li, Lijie Fan, Yuan Yuan, Dina Katabi

Thus, in this paper, we explore the feasibility of adapting RGB-based unsupervised representation learning to RF signals.

Action Recognition Contrastive Learning +3

Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher

no code implementations12 Jun 2022 Juncheng Wang, Junyu Gao, Yuan Yuan, Qi Wang

The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic.

Iterative Genetic Improvement: Scaling Stochastic Program Synthesis

no code implementations26 Feb 2022 Yuan Yuan, Wolfgang Banzhaf

In cases where large programs are required for a solution, it is generally believed that {\it stochastic} search has advantages over other classes of search techniques.

Program Synthesis

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

1 code implementation CVPR 2022 Yangtao Wang, Xi Shen, Shell Hu, Yuan Yuan, James Crowley, Dominique Vaufreydaz

For unsupervised saliency detection, we improve IoU for 4. 9%, 5. 2%, 12. 9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art.

 Ranked #1 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Localization Accuracy metric)

object-detection Object Discovery +5

Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework

1 code implementation31 Jan 2022 Hannah Peeler, Shuyue Stella Li, Andrew N. Sloss, Kenneth N. Reid, Yuan Yuan, Wolfgang Banzhaf

In this paper we introduce Shackleton as a generalized framework enabling the application of linear genetic programming -- a technique under the umbrella of evolutionary algorithms -- to a variety of use cases.

Targeted Supervised Contrastive Learning for Long-Tailed Recognition

1 code implementation CVPR 2022 Tianhong Li, Peng Cao, Yuan Yuan, Lijie Fan, Yuzhe Yang, Rogerio Feris, Piotr Indyk, Dina Katabi

This forces all classes, including minority classes, to maintain a uniform distribution in the feature space, improves class boundaries, and provides better generalization even in the presence of long-tail data.

Contrastive Learning Long-tail Learning

Adaptive Shrink-Mask for Text Detection

no code implementations18 Nov 2021 Chuang Yang, Mulin Chen, Yuan Yuan, Qi Wang, Xuelong Li

It weakens the coupling of texts to shrink-masks, which improves the robustness of detection results.

ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition

no code implementations14 Oct 2021 Zhitong Xiong, Yuan Yuan, Qi Wang

Discriminative local theme-level and object-level representations can be selected with the DLFS module from the spatially-correlated multi-modal RGB-D features.

Scene Classification Scene Recognition

LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds

no code implementations10 Oct 2021 Qi Wang, Tao Han, Junyu Gao, Yuan Yuan, Xuelong Li

The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map.

Visual Crowd Analysis

Investigating and Modeling the Dynamics of Long Ties

1 code implementation22 Sep 2021 Ding Lyu, Yuan Yuan, Lin Wang, Xiaofan Wang, Alex Pentland

Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks.

MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening

no code implementations21 Sep 2021 Yuan Yuan, Yi Sun, Yuanlin Zhang

A novel Spectral-Spatial (SS) stream is established to hierarchically derive and fuse the multi-level prior spectral and spatial expertise from the MS stream and the PAN stream.

Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification

no code implementations12 Sep 2021 Qi Wang, Sikai Bai, Junyu Gao, Yuan Yuan, Xuelong Li

In addition, due to domain gaps between different datasets, the performance is dramatically decreased when re-ID models pre-trained on label-rich datasets (source domain) are directly applied to other unlabeled datasets (target domain).

Person Re-Identification Unsupervised Domain Adaptation

MT: Multi-Perspective Feature Learning Network for Scene Text Detection

no code implementations12 May 2021 Chuang Yang, Mulin Chen, Yuan Yuan, Qi Wang

Text detection, the key technology for understanding scene text, has become an attractive research topic.

Scene Text Detection

Weighted Hierarchical Sparse Representation for Hyperspectral Target Detection

no code implementations11 May 2021 Chenlu Wei, Zhiyu Jiang, Yuan Yuan

However, background dictionary building issue and the correlation analysis of target and background dictionary issue have not been well studied.

Instance-aware Remote Sensing Image Captioning with Cross-hierarchy Attention

no code implementations11 May 2021 Chengze Wang, Zhiyu Jiang, Yuan Yuan

The spatial attention is a straightforward approach to enhance the performance for remote sensing image captioning.

Image Captioning

Deep feature selection-and-fusion for RGB-D semantic segmentation

no code implementations10 May 2021 Yuejiao Su, Yuan Yuan, Zhiyu Jiang

Scene depth information can help visual information for more accurate semantic segmentation.

Semantic Segmentation

Self-supervised spectral matching network for hyperspectral target detection

no code implementations10 May 2021 Can Yao, Yuan Yuan, Zhiyu Jiang

In order to learn more discriminative features, a pair-based loss is adopted to minimize the distance between target pixels while maximizing the distances between target and background.

BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network

no code implementations11 Apr 2021 Chuang Yang, Mulin Chen, Yuan Yuan, Qi Wang

Specifically, a new text representation strategy is proposed to represent text contours from a top-down perspective, which can fit highly curved text contours effectively.

Object Detection

Bio-Inspired Representation Learning for Visual Attention Prediction

no code implementations9 Mar 2021 Yuan Yuan, Hailong Ning, Xiaoqiang Lu

In this paper, a novel VAP method is proposed to generate visual attention map via bio-inspired representation learning.

Representation Learning

Learning Blood Oxygen from Respiration Signals

no code implementations1 Jan 2021 Hao He, Ying-Cong Chen, Yuan Yuan, Dina Katabi

Further, since breathing can be monitored without body contact by analyzing the radio signal in the environment, we show that oxygen too can be monitored without any wearable devices.

Addressing Feature Suppression in Unsupervised Visual Representations

no code implementations17 Dec 2020 Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Rogerio Feris, Piotr Indyk, Dina Katabi

However, contrastive learning is susceptible to feature suppression, i. e., it may discard important information relevant to the task of interest, and learn irrelevant features.

Contrastive Learning Representation Learning

Learning Independent Instance Maps for Crowd Localization

1 code implementation8 Dec 2020 Junyu Gao, Tao Han, Qi Wang, Yuan Yuan, Xuelong Li

Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps.


CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection

no code implementations30 Nov 2020 Chuang Yang, Mulin Chen, Zhitong Xiong, Yuan Yuan, Qi Wang

Extensive experiments demonstrate the proposed CM is efficient and robust to fit arbitrary-shaped text instances, and also validate the effectiveness of MPF and constraints loss for discriminative text features recognition.

Subgroup-based Rank-1 Lattice Quasi-Monte Carlo

no code implementations NeurIPS 2020 Yueming Lyu, Yuan Yuan, Ivor W. Tsang

We theoretically prove a lower and an upper bound of the minimum pairwise distance of any non-degenerate rank-1 lattice.

Bayesian Inference

Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests

1 code implementation19 Oct 2020 Yuan Yuan, Kristen M. Altenburger, Farshad Kooti

Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the assignment conditions of neighbors.

Social and Information Networks Applications

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning

1 code implementation NeurIPS 2020 Tao Han, Junyu Gao, Yuan Yuan, Qi Wang

In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating.

Template Matching

In-Home Daily-Life Captioning Using Radio Signals

no code implementations ECCV 2020 Lijie Fan, Tianhong Li, Yuan Yuan, Dina Katabi

This paper aims to caption daily life --i. e., to create a textual description of people's activities and interactions with objects in their homes.

Privacy Preserving Video Captioning

Pixel-wise Crowd Understanding via Synthetic Data

no code implementations30 Jul 2020 Qi. Wang, Junyu. Gao, Wei. Lin, Yuan Yuan

To be specific, 1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; 2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels.

Crowd Counting Domain Adaptation

Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training

no code implementations CVPR 2020 Yuan Yuan, Wei Su, Dandan Ma

In order to remove the non-uniform blur of images captured from dynamic scenes, many deep learning based methods design deep networks for large receptive fields and strong fitting capabilities, or use multi-scale strategy to deblur image on different scales gradually.

Deblurring Image Restoration +1

Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

1 code implementation5 Apr 2020 Qi. Wang, Tao Han, Junyu. Gao, Yuan Yuan

Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters.

Crowd Counting Domain Adaptation +1

Learning Longterm Representations for Person Re-Identification Using Radio Signals

no code implementations CVPR 2020 Lijie Fan, Tianhong Li, Rongyao Fang, Rumen Hristov, Yuan Yuan, Dina Katabi

RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape.

Person Re-Identification Privacy Preserving

Focus on Semantic Consistency for Cross-domain Crowd Understanding

no code implementations20 Feb 2020 Tao Han, Junyu. Gao, Yuan Yuan, Qi. Wang

According to the semantic consistency, a similar distribution in deep layer's features of the synthetic and real-world crowd area, we first introduce a semantic extractor to effectively distinguish crowd and background in high-level semantic information.

Domain Adaptation

Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance

no code implementations8 Dec 2019 Junyu. Gao, Yuan Yuan, Qi Wang

To reduce the gap, in this paper, we propose a domain-adaptation-style crowd counting method, which can effectively adapt the model from synthetic data to the specific real-world scenes.

Crowd Counting Density Estimation +1

Towards a Proactive MWE Terminological Platform for Cross-Lingual Mediation in the Age of Big Data

no code implementations RANLP 2019 Benjamin K. Tsou, Kapo Chow, JUNRU Nie, Yuan Yuan

It has broader economic implication in the Age of Big Data (Tsou et al, 2015) and Trade War, as the workload, if not, the challenges, increasingly cannot be met by currently available front-line translators.


SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting

no code implementations10 Aug 2019 Junyu. Gao, Qi. Wang, Yuan Yuan

The latter attempts to extract more discriminative features among different channels, which aids model to pay attention to the head region, the core of crowd scenes.

Crowd Counting regression

Gift Contagion in Online Groups: Evidence From WeChat Red Packets

no code implementations24 Jun 2019 Yuan Yuan, Tracy Liu, Chenhao Tan, Qian Chen, Alex Pentland, Jie Tang

Using data on 36 million online red packet gifts on China's social site WeChat, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.

Experimental Design Marketing

Efficient Batch Black-box Optimization with Deterministic Regret Bounds

no code implementations24 May 2019 Yueming Lyu, Yuan Yuan, Ivor W. Tsang

In this work, we investigate black-box optimization from the perspective of frequentist kernel methods.

VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

no code implementations5 May 2019 Yuan Yuan, Zhitong Xiong, Student Member, Qi. Wang, Senior Member, IEEE

Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance.

object-detection Object Detection +1

A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling

no code implementations5 May 2019 Qi. Wang, Junyu. Gao, Yuan Yuan

Our contributions are threefold: (1) A priori s-CNNs model that learns priori location information at superpixel level is proposed to describe various objects discriminatingly; (2) A hierarchical data augmentation method is presented to alleviate dataset bias in the priori s-CNNs training stage, which improves foreground objects labeling significantly; (3) A soft restricted MRF energy function is defined to improve the priori s-CNNs model's labeling performance and reduce the over smoothness at the same time.

Autonomous Driving Data Augmentation +2

Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles

no code implementations5 May 2019 Chengze Wang, Yuan Yuan, Qi. Wang

In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles.

Monocular Visual Odometry Self-Supervised Learning

Cross-Modal Message Passing for Two-stream Fusion

no code implementations30 Apr 2019 Dong Wang, Yuan Yuan, Qi. Wang

The classification object ensures that each modal network predicts the true action category while the competing objective encourages each modal network to outperform the other one.

Action Recognition General Classification +2

Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction

no code implementations30 Apr 2019 Yuan Yuan, Dong Wang, Qi. Wang

3) Results of motion orientation and magnitude are adaptively weighted and fused by a Bayesian model, which makes the proposed method more robust and handle more kinds of abnormal events.

Anomaly Detection Autonomous Vehicles

Memory-Augmented Temporal Dynamic Learning for Action Recognition

no code implementations30 Apr 2019 Yuan Yuan, Dong Wang, Qi. Wang

Human actions captured in video sequences contain two crucial factors for action recognition, i. e., visual appearance and motion dynamics.

Action Recognition Temporal Action Localization

Early Action Prediction with Generative Adversarial Networks

no code implementations30 Apr 2019 Dong Wang, Yuan Yuan, Qi. Wang

Action Prediction is aimed to determine what action is occurring in a video as early as possible, which is crucial to many online applications, such as predicting a traffic accident before it happens and detecting malicious actions in the monitoring system.

Early Action Prediction

Forward Vehicle Collision Warning Based on Quick Camera Calibration

no code implementations22 Apr 2019 Yuwei Lu, Yuan Yuan, Qi. Wang

Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles.

Autonomous Vehicles Camera Calibration

Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection

no code implementations22 Apr 2019 Yuan Yuan, Yuwei Lu, Qi. Wang

In the detection stage, we present a sequential detection model to deal with serious occlusions.

Learning from Synthetic Data for Crowd Counting in the Wild

no code implementations CVPR 2019 Qi. Wang, Junyu. Gao, Wei. Lin, Yuan Yuan

Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations.

Crowd Counting Domain Adaptation

Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising

no code implementations6 Sep 2018 Yuan Yuan, Xiaojing Dong, Chen Dong, Yiwen Sun, Zhenyu Yan, Abhishek Pani

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 sponsored search in the online advertising industry.

Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks

1 code implementation3 Sep 2018 Yuan Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, Liang Lin

We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.

Image Super-Resolution Image-to-Image Translation +1

Robustness Analysis of Pedestrian Detectors for Surveillance

1 code implementation12 Jul 2018 Yuming Fang, Guanqun Ding, Yuan Yuan, Weisi Lin, Haiwen Liu

In this study, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation.

Pedestrian Detection

Temporal Dynamic Graph LSTM for Action-driven Video Object Detection

no code implementations ICCV 2017 Yuan Yuan, Xiaodan Liang, Xiaolong Wang, Dit-yan Yeung, Abhinav Gupta

A common issue, however, is that objects of interest that are not involved in human actions are often absent in global action descriptions known as "missing label".

object-detection Object Recognition +2

Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results

no code implementations8 Jun 2017 Yuan Yuan, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Bingshui Da, Qingfu Zhang, Kay Chen Tan, Yaochu Jin, Hisao Ishibuchi

In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.

Multiobjective Optimization

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