Search Results for author: Wenxi Liu

Found 20 papers, 6 papers with code

End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps

1 code implementation31 Mar 2022 Ke Guo, Wenxi Liu, Jia Pan

In this paper, we aim to forecast a future trajectory distribution of a moving agent in the real world, given the social scene images and historical trajectories.

Semi-Supervised Domain Generalization in Real World:New Benchmark and Strong Baseline

no code implementations19 Nov 2021 Luojun Lin, Han Xie, Zhifeng Yang, Zhishu Sun, Wenxi Liu, Yuanlong Yu, WeiJie Chen, Shicai Yang, Di Xie

In this paper, we introduce a novel task, termed as semi-supervised domain generalization, to study how to interact the labeled and unlabeled domains, and establish two benchmarks including a web-crawled dataset, which poses a novel yet realistic challenge to push the limits of existing technologies.

Domain Generalization

Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck

no code implementations CVPR 2021 Anguo Zhang, Yueming Gao, Yuzhen Niu, Wenxi Liu, Yongcheng Zhou

Person re-identification (Re-ID) is to retrieve a particular person captured by different cameras, which is of great significance for security surveillance and pedestrian behavior analysis.

Person Re-Identification

Reciprocal Transformations for Unsupervised Video Object Segmentation

1 code implementation CVPR 2021 Sucheng Ren, Wenxi Liu, Yongtuo Liu, Haoxin Chen, Guoqiang Han, Shengfeng He

Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers.

Ranked #3 on Unsupervised Video Object Segmentation on DAVIS 2016 (using extra training data)

Optical Flow Estimation Semantic Segmentation +2

An Intelligent CNN-VAE Text Representation Technology Based on Text Semantics for Comprehensive Big Data

no code implementations28 Aug 2020 Genggeng Liu, Canyang Guo, Lin Xie, Wenxi Liu, Naixue Xiong, Guolong Chen

CNN is used to extract the features of text vector to get the semantics among words and VAE is introduced to make the text feature space more consistent with Gaussian distribution.

Text Classification

Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes

no code implementations ECCV 2020 Lei Yang, Wenxi Liu, Zhiming Cui, Nenglun Chen, Wenping Wang

We propose an unsupervised learning framework with the pretext task of finding dense correspondences between point cloud shapes from the same category based on the cycle-consistency formulation.

HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions

1 code implementation3 Jul 2020 Yuzhen Niu, Jianbin Wu, Wenxi Liu, Wenzhong Guo, Rynson W. H. Lau

To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.

HDR Reconstruction Image Reconstruction

Recurrent Distillation based Crowd Counting

no code implementations14 Jun 2020 Yue Gu, Wenxi Liu

Besides, leveraging our density map generation method, we propose an iterative distillation algorithm to progressively enhance our model with identical network structures, without significantly sacrificing the dimension of the output density maps.

Crowd Counting

Over-crowdedness Alert! Forecasting the Future Crowd Distribution

no code implementations9 Jun 2020 Yuzhen Niu, Weifeng Shi, Wenxi Liu, Shengfeng He, Jia Pan, Antoni B. Chan

In this paper, we formulate a novel crowd analysis problem, in which we aim to predict the crowd distribution in the near future given sequential frames of a crowd video without any identity annotations.

Learning Resilient Behaviors for Navigation Under Uncertainty

no code implementations22 Oct 2019 Tingxiang Fan, Pinxin Long, Wenxi Liu, Jia Pan, Ruigang Yang, Dinesh Manocha

Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically.

Autonomous Driving

Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

no code implementations ICCV 2019 Xiaosheng Yan, Yuanlong Yu, Feigege Wang, Wenxi Liu, Shengfeng He, Jia Pan

We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles.

Enhancement Mask for Hippocampus Detection and Segmentation

no code implementations12 Feb 2019 Dengsheng Chen, Wenxi Liu, You Huang, Tong Tong, Yuanlong Yu

Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging.

Hippocampus

Deformable Object Tracking with Gated Fusion

no code implementations27 Sep 2018 Wenxi Liu, Yibing Song, Dengsheng Chen, Shengfeng He, Yuanlong Yu, Tao Yan, Gerhard P. Hancke, Rynson W. H. Lau

In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.

Object Tracking

An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance

no code implementations27 Sep 2018 Wenxi Liu, Yuanlong Yu, Chun-Yang Zhang, Genggeng Liu, Naixue Xiong

To our best knowledge, this is the first attempt to analyze individual extraversion of crowd motions based on trajectories.

Active Learning Event Detection

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

2 code implementations28 Sep 2017 Pinxin Long, Tingxiang Fan, Xinyi Liao, Wenxi Liu, Hao Zhang, Jia Pan

We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system.

reinforcement-learning

Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation

no code implementations22 Sep 2016 Pinxin Long, Wenxi Liu, Jia Pan

We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations.

Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories

no code implementations31 Mar 2016 Wenxi Liu, Rynson W. H. Lau, Xiaogang Wang, Dinesh Manocha

Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature.

Multi-Label Classification

Leveraging Long-Term Predictions and Online-Learning in Agent-based Multiple Person Tracking

no code implementations10 Feb 2014 Wenxi Liu, Antoni B. Chan, Rynson W. H. Lau, Dinesh Manocha

We present a multiple-person tracking algorithm, based on combining particle filters and RVO, an agent-based crowd model that infers collision-free velocities so as to predict pedestrian's motion.

Frame online learning

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