no code implementations • 17 Feb 2023 • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo
Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder.
1 code implementation • 9 Dec 2022 • Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, Liefeng Bo
In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on the methodology of neural radiance fields (NeRF).
1 code implementation • 29 Nov 2022 • Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Liefeng Bo
Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering.
no code implementations • 2 Apr 2022 • Akash Gupta, Jingen Liu, Liefeng Bo, Amit K. Roy-Chowdhury, Tao Mei
To incorporate this ability in intelligent systems a question worth pondering upon is how exactly do we anticipate?
no code implementations • 26 Jan 2022 • Houpu Yao, Jiazhou Wang, Peng Dai, Liefeng Bo, Yanqing Chen
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations.
1 code implementation • IJCAI 2021 • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo, Xiyue Zhang, Tianyi Chen
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.
1 code implementation • 7 Jan 2022 • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Mengyin Lu, Liefeng Bo
Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data.
1 code implementation • 8 Oct 2021 • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, Liefeng Bo
The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods.
1 code implementation • 8 Oct 2021 • Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, Yu Zheng
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.
1 code implementation • 8 Oct 2021 • Xiaoling Long, Chao Huang, Yong Xu, Huance Xu, Peng Dai, Lianghao Xia, Liefeng Bo
To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, em-powering SMIN to maintain dedicated representations for multi-faceted user- and item-wise dependencies.
1 code implementation • 8 Oct 2021 • Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.
1 code implementation • 8 Oct 2021 • Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, Yanfang Ye
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.
1 code implementation • 8 Oct 2021 • Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang
The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.
1 code implementation • 8 Oct 2021 • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo
In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.
no code implementations • 26 Sep 2021 • Qingsong Zhang, Bin Gu, Cheng Deng, Songxiang Gu, Liefeng Bo, Jian Pei, Heng Huang
To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i. e. AsySQN-SGD, -SVRG and -SAGA, are proposed.
1 code implementation • CVPR 2022 • Jiyang Yu, Jingen Liu, Liefeng Bo, Tao Mei
Those methods achieve limited performance as they suffer from the challenge in spatial frame alignment and the lack of useful information from similar LR neighbor frames.
3 code implementations • 8 Jul 2021 • Bo Liu, Chaowei Tan, Jiazhou Wang, Tao Zeng, Huasong Shan, Houpu Yao, Heng Huang, Peng Dai, Liefeng Bo, Yanqing Chen
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
1 code implementation • CVPR 2021 • Longyin Wen, Dawei Du, Pengfei Zhu, QinGhua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu
To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd, formed by 112 video clips with 33, 600 HD frames in various scenarios.
1 code implementation • 4 Mar 2021 • Guanghan Ning, Guang Chen, Chaowei Tan, Si Luo, Liefeng Bo, Heng Huang
We propose a new offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
2 code implementations • 4 Jan 2021 • Le Fang, Tao Zeng, Chaochun Liu, Liefeng Bo, Wen Dong, Changyou Chen
In this paper, we advocate to revive latent variable modeling, essentially the power of representation learning, in the era of Transformers to enhance controllability without hurting state-of-the-art generation effectiveness.
1 code implementation • 4 Jan 2021 • Le Fang, Tao Zeng, Chaochun Liu, Liefeng Bo, Wen Dong, Changyou Chen
Our paper is among the first ones by our knowledge to propose a model and to create datasets for the task of "outline to story".
no code implementations • 27 May 2020 • Guang Chen, Shiwen Shen, Longyin Wen, Si Luo, Liefeng Bo
Existing methods only focused on pig counting using single image, and its accuracy is challenged by several factors, including pig movements, occlusion and overlapping.
no code implementations • 23 Dec 2019 • Yuxiang Ren, Hao Zhu, Jiawei Zhang, Peng Dai, Liefeng Bo
Existing fraud detection methods try to identify unexpected dense subgraphs and treat related nodes as suspicious.
1 code implementation • 4 Dec 2019 • Longyin Wen, Dawei Du, Pengfei Zhu, QinGhua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu
This paper proposes a space-time multi-scale attention network (STANet) to solve density map estimation, localization and tracking in dense crowds of video clips captured by drones with arbitrary crowd density, perspective, and flight altitude.
1 code implementation • 19 Nov 2019 • Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, Jiawei Zhang
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Ranked #7 on Heterogeneous Node Classification on DBLP (PACT) 14k
1 code implementation • International Conference on Computer Vision Workshops 2019 • Dawei Du, Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Lin, QinGhua Hu, Tao Peng, Jiayu Zheng, Xinyao Wang, Yue Zhang, Liefeng Bo, Hailin Shi, Rui Zhu, Aashish Kumar, Aijin Li, Almaz Zinollayev, Anuar Askergaliyev, Arne Schumann, Binjie Mao, Byeongwon Lee, Chang Liu, Changrui Chen, Chunhong Pan, Chunlei Huo, Da Yu, Dechun Cong, Dening Zeng, Dheeraj Reddy Pailla, Di Li, Dong Wang, Donghyeon Cho, Dongyu Zhang, Furui Bai, George Jose, Guangyu Gao, Guizhong Liu, Haitao Xiong, Hao Qi, Haoran Wang, Heqian Qiu, Hongliang Li, Huchuan Lu, Ildoo Kim, Jaekyum Kim, Jane Shen, Jihoon Lee, Jing Ge, Jingjing Xu, Jingkai Zhou, Jonas Meier, Jun Won Choi, Junhao Hu, Junyi Zhang, Junying Huang, Kaiqi Huang, Keyang Wang, Lars Sommer, Lei Jin, Lei Zhang
Results of 33 object detection algorithms are presented.
7 code implementations • ICCV 2019 • Xinyao Wang, Liefeng Bo, Li Fuxin
Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels.
Ranked #6 on Face Alignment on 300W
1 code implementation • CVPR 2019 • Kai Xu, Longyin Wen, Guorong Li, Liefeng Bo, Qingming Huang
Specifically, the temporal coherence branch pretrained in an adversarial fashion from unlabeled video data, is designed to capture the dynamic appearance and motion cues of video sequences to guide object segmentation.
Ranked #2 on Semi-Supervised Video Object Segmentation on YouTube
Semantic Segmentation Semi-Supervised Video Object Segmentation +3
1 code implementation • CVPR 2019 • Rui Zhu, Shifeng Zhang, Xiaobo Wang, Longyin Wen, Hailin Shi, Liefeng Bo, Tao Mei
Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence.
no code implementations • CVPR 2013 • Liefeng Bo, Xiaofeng Ren, Dieter Fox
Complex real-world signals, such as images, contain discriminative structures that differ in many aspects including scale, invariance, and data channel.
no code implementations • NeurIPS 2012 • Shulin Yang, Liefeng Bo, Jue Wang, Linda G. Shapiro
It differs from recognition of basic categories, such as humans, tables, and computers, in that there are global similarities in shape or structure shared within a category, and the differences are in the details of the object parts.
no code implementations • NeurIPS 2012 • Ren Xiaofeng, Liefeng Bo
Finding contours in natural images is a fundamental problem that serves as the basis of many tasks such as image segmentation and object recognition.
no code implementations • 27 Jun 2012 • Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, Dieter Fox
As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them.
no code implementations • NeurIPS 2010 • Liefeng Bo, Xiaofeng Ren, Dieter Fox
We highlight the kernel view of orientation histograms, and show that they are equivalent to a certain type of match kernels over image patches.
no code implementations • International Journal of Computer Vision 2010 • Liefeng Bo, Cristian Sminchisescu
We describe twin Gaussian processes (TGP), a generic structured prediction method that uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and estimates outputs by minimizing the Kullback-Leibler divergence between two GP modeled as normal distributions over finite index sets of training and testing examples, emphasizing the goal that similar inputs should produce similar percepts and this should hold, on average, between their marginal distributions.
Ranked #21 on 3D Human Pose Estimation on HumanEva-I
no code implementations • NeurIPS 2009 • Jian Peng, Liefeng Bo, Jinbo Xu
To model the nonlinear relationship between input features and outputs we propose Conditional Neural Fields (CNF), a new conditional probabilistic graphical model for sequence labeling.
no code implementations • NeurIPS 2009 • Liefeng Bo, Cristian Sminchisescu
To address this problem, we propose an efficient match kernel (EMK), which maps local features to a low dimensional feature space, average the resulting feature vectors to form a set-level feature, then apply a linear classifier.