Search Results for author: Fang Zhao

Found 31 papers, 10 papers with code

Region Graph Embedding Network for Zero-Shot Learning

no code implementations ECCV 2020 Guo-Sen Xie, Li Liu, Fan Zhu, Fang Zhao, Zheng Zhang, Yazhou Yao, Jie Qin, Ling Shao

To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.

Graph Embedding Zero-Shot Learning

Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification

no code implementations ECCV 2020 Fang Zhao, Shengcai Liao, Guo-Sen Xie, Jian Zhao, Kaihao Zhang, Ling Shao

On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks.

Clustering Person Re-Identification +2

OccupancyDETR: Making Semantic Scene Completion as Straightforward as Object Detection

1 code implementation15 Sep 2023 Yupeng Jia, Jie He, Runze Chen, Fang Zhao, Haiyong Luo

Visual-based 3D semantic occupancy perception (also known as 3D semantic scene completion) is a new perception paradigm for robotic applications like autonomous driving.

3D Semantic Scene Completion Autonomous Driving +2

Learning Anchor Transformations for 3D Garment Animation

no code implementations CVPR 2023 Fang Zhao, Zekun Li, Shaoli Huang, Junwu Weng, Tianfei Zhou, Guo-Sen Xie, Jue Wang, Ying Shan

Once the anchor transformations are found, per-vertex nonlinear displacements of the garment template can be regressed in a canonical space, which reduces the complexity of deformation space learning.

Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry

1 code implementation CVPR 2023 Jiaxu Zhang, Junwu Weng, Di Kang, Fang Zhao, Shaoli Huang, Xuefei Zhe, Linchao Bao, Ying Shan, Jue Wang, Zhigang Tu

Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing.

motion retargeting

Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation

1 code implementation CVPR 2022 Tianfei Zhou, Meijie Zhang, Fang Zhao, Jianwu Li

Particularly, we propose i) semantic contrast to drive network learning by contrasting massive categorical object regions, leading to a more holistic object pattern understanding, and ii) semantic aggregation to gather diverse relational contexts in the memory to enrich semantic representations.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning

1 code implementation20 Jan 2022 Chenxing Wang, Fang Zhao, Haichao Zhang, Haiyong Luo, Yanjun Qin, Yuchen Fang

To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module.

Meta-Learning Travel Time Estimation

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e. g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs.

Graph Attention Time Series Analysis

CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, Chenxing Wang

Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.

DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting

no code implementations4 Dec 2021 Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, Chenxing Wang

In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously.

Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction

1 code implementation ICCV 2021 Fang Zhao, Wenhao Wang, Shengcai Liao, Ling Shao

While single-view 3D reconstruction has made significant progress benefiting from deep shape representations in recent years, garment reconstruction is still not solved well due to open surfaces, diverse topologies and complex geometric details.

Garment Reconstruction Single-View 3D Reconstruction

A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation

no code implementations6 Jul 2021 Runze Chen, Haiyong Luo, Fang Zhao, Xuechun Meng, Zhiqing Xie, Yida Zhu

The comparative experiments of knowledge distillation on six public datasets also demonstrate that the SMLDist outperforms other advanced knowledge distillation methods of students' performance, which verifies the good generalization of the SMLDist on other classification tasks, including but not limited to HAR.

Classification Human Activity Recognition +2

Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior

no code implementations9 May 2021 Kaihao Zhang, Wenhan Luo, Yanjiang Yu, Wenqi Ren, Fang Zhao, Changsheng Li, Lin Ma, Wei Liu, Hongdong Li

We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image.

Benchmarking Rain Removal +1

Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency

1 code implementation NeurIPS 2020 Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao

This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image.

Human Parsing Image to 3D +2

Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond

1 code implementation11 Jun 2020 Wenhao Wang, Fang Zhao, Shengcai Liao, Ling Shao

This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB), which can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels.

Clustering Image Classification +3

Multi-Prototype Networks for Unconstrained Set-based Face Recognition

no code implementations13 Feb 2019 Jian Zhao, Jianshu Li, Xiaoguang Tu, Fang Zhao, Yuan Xin, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng

In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.

Face Recognition

Dynamic Conditional Networks for Few-Shot Learning

no code implementations ECCV 2018 Fang Zhao, Jian Zhao, Shuicheng Yan, Jiashi Feng

This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i. e, only a few training samples are available for each condition.

Face Generation Few-Shot Learning +3

Weakly Supervised Phrase Localization With Multi-Scale Anchored Transformer Network

no code implementations CVPR 2018 Fang Zhao, Jianshu Li, Jian Zhao, Jiashi Feng

In this paper, we propose a novel weakly supervised model, Multi-scale Anchored Transformer Network (MATN), to accurately localize free-form textual phrases with only image-level supervision.

Region Proposal

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training

no code implementations16 Nov 2017 Jianshu Li, Shengtao Xiao, Fang Zhao, Jian Zhao, Jianan Li, Jiashi Feng, Shuicheng Yan, Terence Sim

Specifically, iFAN achieves an overall F-score of 91. 15% on the Helen dataset for face parsing, a normalized mean error of 5. 81% on the MTFL dataset for facial landmark localization and an accuracy of 45. 73% on the BNU dataset for emotion recognition with a single model.

Face Alignment Face Parsing +1

Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

no code implementations27 Dec 2016 Fang Zhao, Jiashi Feng, Jian Zhao, Wenhan Yang, Shuicheng Yan

The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches.

Face Recognition

Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution

no code implementations29 Apr 2016 Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo, Shuicheng Yan

To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details.

Image Super-Resolution

Relevance Topic Model for Unstructured Social Group Activity Recognition

no code implementations NeurIPS 2013 Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan

Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data.

Group Activity Recognition Variational Inference

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