no code implementations • 20 Apr 2024 • Kefan Li, Yuan Yuan
As a complementary aspect to code generation, test case generation is of crucial importance in ensuring the quality and reliability of code.
no code implementations • 12 Mar 2024 • Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Robby T. Tan
By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors.
no code implementations • 1 Mar 2024 • Zhengyu Zhang, Ruisi He, Mi Yang, Xuejian Zhang, Ziyi Qi, Yuan Yuan, Bo Ai
Recently, deep learning enabled semantic communications have been developed to understand transmission content from semantic level, which realize effective and accurate information transfer.
no code implementations • 1 Mar 2024 • Zhengyu Zhang, Ruisi He, Bo Ai, Mi Yang, Xuejian Zhang, Ziyi Qi, Yuan Yuan
Integrated sensing and communication (ISAC) is a promising technology for 6G, with the goal of providing end-to-end information processing and inherent perception capabilities for future communication systems.
1 code implementation • 19 Feb 2024 • Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions.
no code implementations • 19 Feb 2024 • Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, Yong Li
Urban spatio-temporal prediction is crucial for informed decision-making, such as transportation management, resource optimization, and urban planning.
1 code implementation • 16 Feb 2024 • Marios Papachristou, Yuan Yuan
Social networks shape opinions, behaviors, and information dissemination in human societies.
no code implementations • 15 Feb 2024 • Chenyang Shao, Fengli Xu, Bingbing Fan, Jingtao Ding, Yuan Yuan, Meng Wang, Yong Li
In this paper, we design a novel Mobility Generation as Reasoning (MobiGeaR) framework that prompts LLM to recursively generate mobility behaviour.
no code implementations • 13 Feb 2024 • Mingyang Li, Hongyu Liu, Yixuan Li, Zejun Wang, Yuan Yuan, Honglin Dai
Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance.
1 code implementation • 18 Jan 2024 • Yang Zhan, Zhitong Xiong, Yuan Yuan
Specifically, after projecting RS visual features to the language domain via an alignment layer, they are fed jointly with task-specific instructions into an LLM-based RS decoder to predict answers for RS open-ended tasks.
no code implementations • 15 Jan 2024 • Xin Yang, Wending Yan, Yuan Yuan, Michael Bi Mi, Robby T. Tan
They struggle to acquire new knowledge while also retaining previously learned knowledge. To address these problems, we propose a semantic segmentation method for multiple adverse weather conditions that incorporates adaptive knowledge acquisition, pseudolabel blending, and weather composition replay.
1 code implementation • 12 Jan 2024 • Haoxuan Ding, Junyu Gao, Yuan Yuan, Qi Wang
Meanwhile, the proposed SamLP has great few-shot and zero-shot learning ability, which shows the potential of transferring vision foundation model.
no code implementations • 1 Jan 2024 • Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Shunli Zhang, Robby Tan
However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts.
no code implementations • 29 Dec 2023 • Xin Zhang, Jinheng Xie, Yuan Yuan, Michael Bi Mi, Robby T. Tan
Further, to ensure the distinguishability among various regions, we introduce a region-level contrastive clustering loss to pull closer similar regions across images.
no code implementations • 23 Dec 2023 • Yan Leng, Yuan Yuan
Recent social science research has explored the use of these ``black-box'' LLM agents for simulating complex social systems and potentially substituting human subjects in experiments.
no code implementations • 19 Dec 2023 • Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, Yong Li
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
1 code implementation • 13 Dec 2023 • Yang Zhan, Yuan Yuan, Zhitong Xiong
To foster this task, we propose Mono3DVG-TR, an end-to-end transformer-based network, which takes advantage of both the appearance and geometry information in text embeddings for multi-modal learning and 3D object localization.
no code implementations • 12 Dec 2023 • Xingwei He, Qianru Zhang, A-Long Jin, Jun Ma, Yuan Yuan, Siu Ming Yiu
Given the lack of paired data (i. e., false claims and their corresponding correct claims), existing methods typically adopt the mask-then-correct paradigm.
no code implementations • 13 Nov 2023 • Zhaojian Li, Bin Zhao, Yuan Yuan
To this end, a metric to measure the spatial perception of audio is proposed for the first time.
no code implementations • 9 Oct 2023 • Yong Lin, Fan Zhou, Lu Tan, Lintao Ma, Jiameng Liu, Yansu He, Yuan Yuan, Yu Liu, James Zhang, Yujiu Yang, Hao Wang
To address this challenge, we then propose Continuous Invariance Learning (CIL), which extracts invariant features across continuously indexed domains.
1 code implementation • 24 Aug 2023 • Yuan Yuan, Yang Zhan, Zhitong Xiong
To address this issue, in this work, we investigate the parameter-efficient transfer learning (PETL) method to effectively and efficiently transfer visual-language knowledge from the natural domain to the RS domain on the image-text retrieval task.
Ranked #3 on Cross-Modal Retrieval on RSICD
no code implementations • 20 Aug 2023 • Shuzhen Chen, Yuan Yuan, Youming Tao, Zhipeng Cai, Dongxiao Yu
Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance.
no code implementations • 18 Aug 2023 • Yuan Yuan, Kristen M. Altenburger
The reliability of controlled experiments, or "A/B tests," can often be compromised due to the phenomenon of network interference, wherein the outcome for one unit is influenced by other units.
no code implementations • 16 Aug 2023 • Shan Huang, Chen Wang, Yuan Yuan, Jinglong Zhao, Jingjing Zhang
We describe the identification assumptions, the estimation strategies, and the inference technique under this framework.
1 code implementation • 3 Aug 2023 • Yeying Jin, Beibei Lin, Wending Yan, Yuan Yuan, Wei Ye, Robby T. Tan
In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions.
no code implementations • 17 Jun 2023 • Huandong Wang, Huan Yan, Can Rong, Yuan Yuan, Fenyu Jiang, Zhenyu Han, Hongjie Sui, Depeng Jin, Yong Li
In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data.
2 code implementations • 21 May 2023 • Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, Yong Li
To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
no code implementations • 17 May 2023 • Hao Yang, Junyu Gao, Yuan Yuan, Xuelong Li
Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task: 1) long temporal data is difficult to extract contextual information with temporal correlation; 2) the anomalous data are rare in time series, causing normal/abnormal imbalance in anomaly detection, making the detector classification degenerate or even fail.
no code implementations • 10 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.
1 code implementation • 9 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.
no code implementations • 12 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.
no code implementations • 6 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.
no code implementations • 2 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.
1 code implementation • 24 Nov 2022 • Xin Yang, Michael Bi Mi, Yuan Yuan, Xin Wang, Robby T. Tan
In our DA framework, we retain the depth and background information during the domain feature alignment.
1 code implementation • 15 Nov 2022 • Yeying Jin, Wei Ye, Wenhan Yang, Yuan Yuan, Robby T. Tan
Most existing methods rely on binary shadow masks, without considering the ambiguous boundaries of soft and self shadows.
1 code implementation • 23 Oct 2022 • Yang Zhan, Zhitong Xiong, Yuan Yuan
However, the object-level visual grounding on RS images is still under-explored.
no code implementations • 2 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).
no code implementations • 1 Sep 2022 • Yangtao Wang, Xi Shen, Yuan Yuan, Yuming Du, Maomao Li, Shell Xu Hu, James L Crowley, Dominique Vaufreydaz
This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
Ranked #4 on Unsupervised Instance Segmentation on COCO val2017
no code implementations • 14 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.
no code implementations • 6 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.
no code implementations • 12 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.
1 code implementation • 9 Apr 2022 • Qiang Li, Yuan Yuan, Xiuping Jia, Qi Wang
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution.
no code implementations • 26 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.
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
1 code implementation • 31 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.
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.
Ranked #22 on Long-tail Learning on CIFAR-10-LT (ρ=100)
no code implementations • 18 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.
no code implementations • 14 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.
no code implementations • 10 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.
1 code implementation • 22 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.
no code implementations • 21 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.
no code implementations • 12 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).
no code implementations • 12 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.
no code implementations • 11 May 2021 • Zhinan Cai, Zhiyu Jiang, Yuan Yuan
(3) a smooth mechanism is utilized to remove some of pseudo-changes and noise.
Change Detection Change detection for remote sensing images +1
no code implementations • 11 May 2021 • Chengze Wang, Zhiyu Jiang, Yuan Yuan
The spatial attention is a straightforward approach to enhance the performance for remote sensing image captioning.
no code implementations • 11 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.
no code implementations • 11 May 2021 • Xinxing He, Yuan Yuan, Zhiyu Jiang
Open set domain recognition has got the attention in recent years.
no code implementations • 10 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.
no code implementations • 10 May 2021 • Yuejiao Su, Yuan Yuan, Zhiyu Jiang
Scene depth information can help visual information for more accurate semantic segmentation.
Ranked #9 on Semantic Segmentation on SUN-RGBD (using extra training data)
no code implementations • 11 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.
no code implementations • 9 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.
no code implementations • 1 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.
no code implementations • 17 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.
1 code implementation • 8 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.
no code implementations • 30 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.
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.
1 code implementation • 19 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
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.
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.
no code implementations • 30 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.
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.
2 code implementations • 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 • Haopeng Li, Yuan Yuan, Qi. Wang
Video frame interpolation achieves temporal super-resolution by generating smooth transitions between frames.
Ranked #14 on Video Frame Interpolation on Vimeo90K
1 code implementation • 5 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.
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.
no code implementations • 20 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.
no code implementations • 8 Dec 2019 • Junyu. Gao, Tao Han, Qi. Wang, Yuan Yuan
Recently, crowd counting using supervised learning achieves a remarkable improvement.
no code implementations • 8 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.
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.
no code implementations • 10 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.
no code implementations • 24 Jun 2019 • Yuan Yuan, Tracy Liu, Chenhao Tan, Qian Chen, Alex Pentland, Jie Tang
Using data on 36 million online red packet gifts on a large social site in East Asia, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.
no code implementations • 24 May 2019 • Yueming Lyu, Yuan Yuan, Ivor W. Tsang
In this work, we investigate black-box optimization from the perspective of frequentist kernel methods.
no code implementations • ICLR 2019 • Yuan Yuan, Yueming Lyu, Xi Shen, Ivor W. Tsang, Dit-yan Yeung
The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion.
Ranked #11 on Weakly Supervised Action Localization on ActivityNet-1.3 (mAP@0.5 metric)
Weakly Supervised Action Localization Weakly-supervised Learning +2
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 5 May 2019 • Qi. Wang, Junyu. Gao, Yuan Yuan
Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving.
no code implementations • 30 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.
no code implementations • 30 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.
no code implementations • 30 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.
no code implementations • 30 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.
no code implementations • 22 Apr 2019 • Yuan Yuan, Yuwei Lu, Qi. Wang
In the detection stage, we present a sequential detection model to deal with serious occlusions.
no code implementations • 22 Apr 2019 • Yuwei Lu, Yuan Yuan, Qi. Wang
Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles.
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.
no code implementations • 6 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.
1 code implementation • 3 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.
1 code implementation • 12 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.
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".
Ranked #3 on Weakly Supervised Object Detection on Charades
no code implementations • 8 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.