no code implementations • CVPR 2023 • Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, BaoCai Yin, Xin Yang
The alignment module is responsible for cross-style and cross-frame-rate alignment between frame and event modalities under the guidance of the moving cues furnished by events.
no code implementations • 19 Apr 2023 • Yang Zhou, Hanjie Wu, Wenxi Liu, Zheng Xiong, Jing Qin, Shengfeng He
In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction.
no code implementations • 15 Nov 2022 • Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanlong Yu, Yuexin Ma, Shengfeng He, Jia Pan
We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding.
no code implementations • 29 May 2022 • Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He
To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem.
1 code implementation • CVPR 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.
Ranked #2 on
Trajectory Prediction
on Stanford Drone
1 code implementation • 19 Nov 2021 • Luojun Lin, Han Xie, Zhishu Sun, WeiJie Chen, Wenxi Liu, Yuanlong Yu, Lei Zhang
From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG.
1 code implementation • ICCV 2021 • Wenxi Liu, Qi Li, Xindai Lin, Weixiang Yang, Shengfeng He, Yuanlong Yu
In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations.
Ranked #2 on
Land Cover Classification
on DeepGlobe
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.
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.
1 code implementation • CVPR 2021 • Weixiang Yang, Qi Li, Wenxi Liu, Yuanlong Yu, Yuexin Ma, Shengfeng He, Jia Pan
Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
Autonomous Driving
Monocular Cross-View Road Scene Parsing(Road)
+2
no code implementations • 28 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.
1 code implementation • 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.
1 code implementation • 3 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.
no code implementations • 14 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.
no code implementations • 9 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.
no code implementations • 22 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.
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.
no code implementations • 12 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.
no code implementations • 27 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.
no code implementations • 27 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.
2 code implementations • 28 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.
no code implementations • 22 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.
no code implementations • 31 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.
no code implementations • 10 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.