Search Results for author: Liefeng Bo

Found 30 papers, 17 papers with code

Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

1 code implementation8 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.

Recommendation Systems

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

1 code implementation8 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.

Traffic Prediction

Social Recommendation with Self-Supervised Metagraph Informax Network

1 code implementation8 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.

Collaborative Filtering Recommendation Systems

Graph Meta Network for Multi-Behavior Recommendation

1 code implementation8 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.

Meta-Learning Recommendation Systems +1

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

1 code implementation8 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.

Collaborative Filtering Recommendation Systems

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

no code implementations8 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.

Multi-Task Learning Session-Based Recommendations

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

1 code implementation8 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.

Graph Attention Recommendation Systems

AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization

no code implementations26 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.

Federated Learning

Memory-Augmented Non-Local Attention for Video Super-Resolution

no code implementations25 Aug 2021 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.

Video Super-Resolution

Spatial-Temporal Sequential Hypergraph Network for Crime Prediction

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.

Crime Prediction

Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform

3 code implementations8 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.

Federated Learning

Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark

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.

Object Detection

Data Augmentation for Object Detection via Differentiable Neural Rendering

1 code implementation4 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.

Data Augmentation Neural Rendering +2

Transformer-based Conditional Variational Autoencoder for Controllable Story Generation

2 code implementations4 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.

Latent Variable Models Representation Learning +1

Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events

1 code implementation4 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".

Keyword Extraction Language Modelling +1

Efficient Pig Counting in Crowds with Keypoints Tracking and Spatial-aware Temporal Response Filtering

no code implementations27 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.


EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph

no code implementations23 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.

Fraud Detection

Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

1 code implementation4 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.

Crowd Counting

Heterogeneous Deep Graph Infomax

1 code implementation19 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.

Classification General Classification +3

Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

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.

Face Alignment Robust Face Alignment

Spatiotemporal CNN for Video Object Segmentation

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.

Semantic Segmentation Semi-Supervised Video Object Segmentation +3

ScratchDet: Training Single-Shot Object Detectors from Scratch

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.

General Classification Object Detection

Multipath Sparse Coding Using Hierarchical Matching Pursuit

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.

Image Classification

Unsupervised Template Learning for Fine-Grained Object Recognition

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.

Object Recognition

Discriminatively Trained Sparse Code Gradients for Contour Detection

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.

BSDS500 Contour Detection +3

Kernel Descriptors for Visual Recognition

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.

Image Classification Scene Recognition

Twin gaussian processes for structured prediction

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.

3D Human Pose Estimation Gaussian Processes +1

Conditional Neural Fields

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.

Handwriting Recognition Hyperparameter Optimization +1

Efficient Match Kernel between Sets of Features for Visual Recognition

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.


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